NAMIC Wiki - User contributions [en]

NewsArchive/2007

2017-01-03T19:56:59Z

Zack: Update from Wiki

Dissemination

2016-12-13T20:53:57Z

Zack: Update from Wiki

__NOTOC__
=Dissemination=
'''Co-PIs: Tina Kapur, Ph.D., BWH and Steve Pieper, Ph.D., Isomics'''

[[Image:Big-Dissemination-Logo.png|150px|left]]
As a component of NA-MIC's Outreach activities, Dissemination is closely allied with Service and Training. Our primary objective is to facilitate others to learn, teach, and perform biomedical and behavioral research using the free and open source (FOSS) NA-MIC Kit, as well as the novel methodologies and techniques developed by NA-MIC investigators. Given the complex, specialized nature of NA-MIC's technology, it is encumbent upon our organization to provide top-notch technical support to individuals with a range of expertise. We complement the pedagogical approach of the Training core, which advances subject matter expertise, by supporting the broad community of biomedical researchers who use NA-MIC technology as a key component of their medical image analysis approach. We support this community through a variety of means: (1) maintaining an extensive web-presence that includes easy access to NA-MIC publications, software, and data, (2) organizing our “flagship” Project Week working events, (3) nurturing an “open organization” through active daily use of our public wiki to organize and document our progress, (4) organizing “birds of a feather” meetings on timely topics, and (5) developing close bi-directional collaborations with external researchers who either use or strengthen the NA-MIC Kit. These efforts actively sustain a community of like-minded researchers whose work, in turn, amplifies the scope and utility of NA-MIC activities.
 

==Focus Areas==
As the overall identity of NA-MIC transitions from a provider of medical image analysis technologies to a provider of integrated biomedical research solutions, these changes are reflected in our organizational objectives.

===Impact through collaborations===
[[Image:HexQual5-Dec06small.png|200 px|left]]
‎In addition to actively encouraging new collaborations, we support a network of 21 funded collaborations. These collaborative groups are actively working to use and expand the NA-MIC Kit to address specific biomedical problems across a wide range of organ systems and pathologies. Access our collaboration partners [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations here.]

===Outreach events===
[[Image:Dissem1.png|250px|left|thumb|Programming session during Project Week]]
The outreach meetings, including hands-on training workshops and working research events, serve the two-fold purpose of building an active user community around the NA-MIC Kit and gathering in-person feedback to improve the materials that form the basis of the online learning resources mentioned above. Each year we offer a host of clinically oriented Project Events, which feature hands-on training and research using the end-to-end solutions developed by previous and current DBPs (i.e., Lupus, Prostate Cancer, Autism, Schizophrenia/velocardiofacial syndrome, Atrial Fibrillation, Huntington's Disease, Head and Neck Cancer, Traumatic Brain Injury).
Access our calendar of upcoming events [http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events here].

Training

2016-12-13T20:53:56Z

Zack: Update from Wiki

__NOTOC__
=Training=
'''PI: Sonia Pujol, Ph.D., BWH'''
[[Image:Big-Training-Logo.png|150px|left]]
The goal of Training is to lower barriers to effective communication between the clinical translational investigators and the computer scientists engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. Training addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. Initially, the primary activity of Training was to develop and deliver hands-on learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. We used components of the NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, that is, clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. Currently, we are expanding our education outreach effort to include a greater proportion of the clinical translational research community.

==Focus Areas==
===On-line learning resources===
[[Image:WhiteMatterExploration.PNG|150px|left]]
Developing new on-line medical image analysis training materials is one of our top priorities. Since 2005, we have organized and delivered more than 70 hands-on workshops at both national venues and international conferences. We are constantly adding to the materials and datasets available through our website. These training materials are developed for specific use cases gleaned from the Driving Biological Projects (DBPs) and require close collaboration among all cores (Outreach, Computer Science, DBPs, and external collaborators). All of our tutorials can be self-taught or administered by an instructor. Each tutorial follows the rubric established in How People Learn [1,2,3], which requires learner-centered, goal-oriented experiential teaching. Access a [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials sampling] of the available tutorials, or the [http://www.slicer.org/slicerWiki/index.php/Slicer_3.6:Training full compendium] for Biomedical Engineers and Clinical Research Users of the NA-MIC Kit.

===Hands-on training===
[[Image:RSNA2011-SlicerWorkshop.jpg|150px|left]]
We offer a variety of hands-on training experiences to increase the impact of our training program. For example, at the 2011 Radiology Society of North America (RSNA) meeting, Slicer 3D was used in two 90-minute courses entitled "3D Visualization of DICOM images for radiological applications" and "Quantitative Medical Imaging for Clinical Research and Practice". Both events were completely subscribed; with standing room only (~100 attendees).
Access our calendar of upcoming events [http://www.na-mic.org/Wiki/index.php/Events here].

===Validation methodology and practice===
[[Image: Xu-MICCAI2010-fig3.png|442px|left|]] Validation plays an important role in the assessment of algorithm performance that benefits both developers and users. Among the challenges of validating segmentation and registration algorithms for patient-specific analyses are (1) definition of appropriate metrics to measure differences among tools and across a sequence of images of the same patient; (2) evaluation of the significance of the differences observed, and (3) comparison to a gold standard, where available. Validation enables developers to assess the performance and limitations of their tools and to identify areas for improvement. In addition, validation provides users with the ability to compare different tools in a standardized way. For example, a retrospective validation analysis of the clinical accuracy of MRI-guided robotic biopsy for prostate cancer [5] developed by the Prostate DBP is shown in the figure. We have developed a portfolio of validation approaches for image segmentation through the organization of Grand Challenge workshops at the Medical Image Computing Computer-Assisted Intervention (MICCAI) Conference, and through our pioneering initiative in the standardized evaluation of single-tensor imaging tractography algorithms, as well as the first DTI Tractography Challenge for Neurosurgical Planning that gathered 8 international research teams at MICCAI 2011 [4,6].

==Suggested Reading==
# John D. Bransford, Ann L. Brown, and Rodney R. Cocking, editors; How People Learn: Brain, Mind, Experience, and School. National Research Council, The National Academies Press, Washington, D.C. 1999.
# Lai I, Gollub R, Hoge R, Greve D, Vangel M, Poldrack R, Greenberg J. Teaching Statistical Analysis of fMRI Data. Proceedings of the American Society for Engineering Education (CD-ROM DEStech Publications) Session 2109: 11 pages, 2003.
# Pujol S., Kikinis R., Gollub R. [http://www.na-mic.org/publications/item/view/1187 Lowering the Barriers Inherent in Translating Advances in Neuroimage Analysis to Clinical Research Applications.] Acad Radiol. 2008 Jan;15(1):114-8. PMID: 18078914. PMCID: PMC2234595.
# Pujol S, Westin CF, Whitaker R, Gerig G, Fletcher T, Magnotta V, Bouix S, Kikinis R, Wells W, Gollub R. Preliminary results on the use of STAPLE for evaluating DT-MRI tractography in the absence of ground truth. Proceedings of the 17th Scientific Meeting of the International Society for Magnetic Resonance in Medicine. 2009.
# Xu H., Lasso A., Vikal S., Guion P., Krieger A., Kaushal A., Whitcomb L.L., Fichtinger G. MRI-guided Robotic Prostate Biopsy: A Clinical Accuracy Validation. Int Conf Med Image Comput Comput Assist Interv. 2010 Sep;13(Pt 3):383-91. PMID: 20879423. PMCID: PMC2976594.
# Pujol S, Kikinis R, Golby A, Gerig G, Styner M, Wells W, Westin CF, Gouttard S, Nabavi A. [http://www.na-mic.org/Wiki/index.php/Events:_DTI_Tractography_Challenge_MICCAI_2011 DTI Tractography for Neurosurgical Planning: A Grand Challenge.] Int Conf Med Image Comput Comput Assist Interv. (MICCAI) 2011, Toronto, Canada.

About NA-MIC

2016-12-13T20:20:38Z

Zack: Update from Wiki

=Organization=

[[image:NIH-NIBIB-Logo.png|200px|left|thumb| NA-MIC is a national research center supported by grant '''U54 EB005149 (EB)''' from the NIBIB NIH HHS.]]

NA-MIC was funded in September of 2004 after submission of an application in response to an [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-022.html RFA] issued by NIH as part of the [http://nihroadmap.nih.gov/ roadmap initiative], which called for the establishment of 7 National Centers for Biomedical Computing. All of the NCBC centers are organized around a series of specialized cores based on the requirements of the funding agency. The Computer Science Core consists of two teams. The [[Algorithms|algorithm]] team develops and implements medical image computing algorithms using the [http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit NA-MIC Kit]. The [[Engineering|engineering]] team develops and maintains the [http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit NA-MIC Kit], a software platform designed to enable research. The [[Driving Biological Projects|driving biological projects]] use the tools provided by the algorithm and engineering cores to develop software solutions that further their biomedical research. The [[Training|training]] and [[Dissemination|dissemination]] cores work on both internal and external outreach. The [[Service|service]] core supports the virtualized IT infrastructure that enables all these activities in a distributed environment. The [[Leadership|leadership]] core is responsible for the overall direction of the alliance. The PI works in close consultation with all the participants in the NA-MIC effort. Since its funding begun, NA-MIC has developed a network of internal and external collaborations. More information about the collaborations can be found on the [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations '''NA-MIC Wiki'''].

<gallery Caption="NA-MIC Cores" >
Image:NAMIC_380x463.jpg|[[Leadership|<big>Leadership</big>]] PI: R. Kikinis SPL, BWH
Image: Big-DBP-Logo.png |[[Driving Biological Projects#2007-2010|<big>DBP</big>]] University of Utah, UT University of Iowa, IA UCLA, CA MGH, HMS, MA
Image: Big-DBP-Logo.png |[[Driving Biological Projects#2004-2007|<big>DBP's, til '10</big>]] PNL, Brockton VA, HMS UCI, CA Dartmouth College, NH Indiana University, Indianapolis U of Toronto, Canada Mind Institute, CA JHU/Queen's University UNC, NC HMS, MA 
Image:Big-Algorithm-Logo.png|[[Algorithms|<big>CS (Algorithms)</big>]] Core PI: R. Whitaker University of Utah, UT MIT, MA UNC, NC Georgia Tech, GA
Image:Big-Engineering-Logo.png|[[Engineering|<big>CS (Engineering)</big>]] Core PI: W. Schroeder Kitware, Inc. BIRN CC, UCSD NRG, WUSTL GRC, GE Isomics, Inc.
Image:Big-Service-Logo.png|[[Service|<big>Service</big>]] Core PI: W. Schroeder Kitware, Inc.
Image:Big-Training-Logo.png|[[Training|<big>Training</big>]] Core PI: S. Pujol SPL, BWH
Image:Big-Dissemination-Logo.png|[[Dissemination|<big>Dissemination</big>]] Core Co-PI: T. Kapur, S. Pieper SPL, BWH, Isomics Inc.
</gallery>

Contact NA-MIC

2016-12-13T20:15:42Z

Zack: Update from Wiki

=Contact Us=

For more information about NA-MIC, please contact:

Ron Kikinis, M.D. 
Surgical Planning Laboratory 
Brigham & Women's Hospital 
1249 Boylston St., Room 352 
Boston, MA 02215 

'''Phone:''' +1 617.732.7389 
'''E-mail:''' kikinis at bwh.harvard.edu

NewsArchive

2016-12-13T19:34:38Z

Zack: Update from Wiki

NewsArchive/2014

2016-06-29T17:30:06Z

Zack: Update from Wiki

__NOTOC__
=News Archive=
[[NewsArchive/2014| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: [[NewsArchive/2011| 2011]] :: [[NewsArchive/2010| 2010]] :: [[NewsArchive/2009| 2009]] :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]

=2014=
==December==
[[Image:RSNABanner_2014.jpg|left|400px|thumb| '''RSNA 2014''' 100th Annual Meeting of the Radiological Society of North America, was held on November 30-December 5 McCormick Place, Chicago.
[http://www.na-mic.org/Wiki/index.php/RSNA_2014 Slicer hands-on courses] also were presented at the meeting. [http://www.rsna.org/Annual_Meeting.aspx Read more...]]]
==September==
[[Image:MICCAI2014.jpg|left|400px|thumb| '''MICCAI 2014''' The 17th International Conference on Medical Image Computing and Computer Assisted Intervention, was held on September 14-18, 2014 at MIT, in Cambridge, MA. MICCAI attracts annually world leading scientists, engineers and clinicians from a wide range of disciplines associated with medical imaging and computer assisted surgery. [http://miccai2014.org/index.html Read more...]]]
==June==
[[Image:PW-MIT2014.png|left|400px|thumb|'''2014 Summer Project Week Event''' The Project Week of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. This 19th Project Event was held on June 23-27, 2014 in Cambridge, MA. The Project Week of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine.
[http://www.na-mic.org/Wiki/index.php/2014_Summer_Project_Week">Read more...]]]
==January==
[[Image:PW-SLC2014.png|left|400px|thumb|'''2014 AHM, EAB and Project Week''' Hosted in Salt Lake City, Utah. January 6-10, 2014. These events, with the exception of the EAB meeting, are open to collaborators and potential collaborators. [http://www.na-mic.org/Wiki/index.php/AHM_2014 Read more...]]]

NewsArchive/2015

2016-06-29T17:30:05Z

Zack: Update from Wiki

__NOTOC__
=News Archive=
[[NewsArchive/2014| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: [[NewsArchive/2011| 2011]] :: [[NewsArchive/2010| 2010]] :: [[NewsArchive/2009| 2009]] :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]

=2015=
==December==
[[Image:RSNA2015Banner.png|left|400px|thumb|RSNA 2015 celebrates RSNA’s 100 years at the forefront of the radiology industry—bringing together the specialty’s professionals for education opportunities and networking, and providing a forum for collaboration on the latest innovations by practitioners and manufacturers alike. The celebration will be held on November 29-December 4, 2015 at McCormick Place in Chicago. [http://www.rsna.org/Annual_Meeting.aspx Read more...]]]

==October==
[[Image:MICCAI2015.png|left|400px|thumb|The annual DTI Challenge workshop brings together the members of the DTI Challenge working group.The workshop was held on October 5, 2015 at the 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in Munich, Germany. [http://projects.iq.harvard.edu/dtichallenge15 Read more...]]]

==June==
[[Image:PW-Summer2015.png|left|400px|thumb|The '''21st PROJECT EVENT''' was held June 21-24, 2015 in Barcelona, Spain. [http://www.na-mic.org/Wiki/index.php/2015_Summer_Project_Week Read more...]]]

==January==
[[image:ferenc-jolesz-2002i.png|left]] [http://www.brighamandwomens.org/about_bwh/publicaffairs/news/publications/DisplayBulletin.aspx?articleid=6530 '''In Memoriam: Ferenc A. Jolesz, MD, B. Leonard Holman Professor of Radiology.''']
 Brigham and Women’s Hospital and the Department of Radiology mourn the passing of Ferenc A. Jolesz, MD, who died suddenly and unexpectedly on December 31st, 2014.
 
==<hr> ==
[[Image:PW-2015SLC.png|left|400px|thumb|'''2015 Project Week''' Hosted in Salt Lake City, Utah. January 5-9, 2015. [http://www.na-mic.org/Wiki/index.php/2015_Winter_Project_Week Read more...]]]

NewsArchive/2012

2016-06-29T17:30:03Z

Zack: Update from Wiki

__NOTOC__

[[NewsArchive| 2014]] :: [[NewsArchive/2013| 2013]] :: '''2012''' :: [[NewsArchive/2011| 2011]] :: [[NewsArchive/2010| 2010]] :: [[NewsArchive/2009| 2009]] :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]
=2012=
==November==
[[Image:RSNA2012-2.jpg|left|400px|thumb| '''RSNA 2012''' The 98th Annual Meeting of the Radiological Society of North America was held on '''November 25-30, 2012''' at McCormick Place, in Chicago, IL. RSNA is an international society of radiologists, medical physicists and other medical professionals with more than 50,000 members across the globe. [http://www.rsna.org/ Read more...]]]
==October==
[[Image:miccai-2012.png|left|400px|thumb| '''MICCAI 2012''' The 15th International Conference on Medical Image Computing and Computer Assisted Intervention, will be held on October 1-5, 2012 in Nice, France, organised by Inria (French Institute for Research in Computer Science and Applied Mathematics). MICCAI attracts annually world leading scientists, engineers and clinicians from a wide range of disciplines associated with medical imaging and computer assisted surgery. [http://www.miccai2012.org Read more...]]]
==June==
[[Image:PW-MIT2012.png|left|400px|thumb|'''2012 Summer Project Week''' The 15th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine, will be hosted at MIT from '''June 18 to June 22, 2012'''. Participants will engage in open source programming using the NA-MIC Kit, algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational biomedical research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. [http://www.na-mic.org/Wiki/index.php/2012_Summer_Project_Week Read more...]]]
==May==
[[Image:slicer4Announcement400px-mj.png|left|400px|thumb|''' Researchers Awarded Fellowships from Harvard Club of Australia''' HARVARDgazette reports that The Harvard Club of Australia Foundation has announced fellowship awards to eight accomplished Harvard researchers intending collaborative scientific research in Australia during 2012. Among the award recipients is Ron Kikinis, MD of Surgical Planning Laboratory at Brigham and Women's Hospital and Harvard Medical School. [http://news.harvard.edu/gazette/story/2012/02/eight-from-harvard-headed-down-underRead more...]]]
==January==
[[Image:SLC-crop.jpg|left|400px|thumb|'''The 2012 NA-MIC All Hands Meeting, External Advisory Board Meeting and 14th Project Event''' Hosted in Salt Lake City, UT. January 9-13, 2012. [http://www.na-mic.org/Wiki/index.php/AHM_2012 Read more...]]]

NewsArchive/2013

2016-06-29T17:30:02Z

Zack: Update from Wiki

__NOTOC__
=News Archive=
[[NewsArchive| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: [[NewsArchive/2011| 2011]] :: [[NewsArchive/2010| 2010]] :: [[NewsArchive/2009| 2009]] :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]

=2013=
==December==
[[image:RSNA2013.png|left|400px|thumb| '''3D Slicer Exhibit and Courses at RSNA 2013''' The 99th Annual Meeting of the Radiological Society of North America will be held on December 1-6, 2013 at McCormick Place, in Chicago, IL. RSNA is an international society of radiologists, medical physicists and other medical professionals with more than 51,000 members across the globe. [http://www.na-mic.org/Wiki/index.php/RSNA_2013 Read more...]]]

==September==
[[image:MICCAI2013.jpg|left|400px|thumb| '''DTI Tractography Challenge''' at [http://www.miccai2013.org/ MICCAI], September 22-26, 2013 in Nagoya, Japan. This workshop on Peritumoral White Matter Anatomy for Neurosurgical Decision-Making aims to evaluate the performances of tractography algorithms in the reconstruction of peritumoral anatomy and corticospinal tract trajectory on pre-operative and post-operative diffusion data from patients presenting with a tumor in or near the motor system. [http://dtichallenge.github.io/miccai2013/ Read more...]]]

==June==
[[image:PW-MIT2013.png|left|400px|thumb| '''The 2013 Summer Project Week Event''' Hosted in Cambridge, MA. June 17-21, 2013. [http://www.na-mic.org/Wiki/index.php/2013_Summer_Project_Week Read more...]]]

==March==
[[image:IGT-NAMIC-NIH-combined-logo-L.png|left|400px|thumb| '''6th NCIGT and NIH Image Guided Therapy Workshop''' This event was held on March 21-23, 2013, in Doubletree by Hilton Washington DC in Crystal City, VA. The topic for this year is Interventional applications for a changing healthcare environment. [http://www.ncigt.org/pages/IGT_Workshop_2013 Read more...]]]

==January==
[[image:PW-SLC2013.png|left|400px|thumb| '''The 2013 NA-MIC All Hands Meeting, External Advisory Board Meeting and 16th Project Event''' Hosted in Salt Lake City, Utah. January 7-11, 2013. [http://www.na-mic.org/Wiki/index.php/AHM_2013 Read more...]]]

NewsArchive/2010

2016-06-29T17:30:01Z

Zack: Update from Wiki

__NOTOC__

[[NewsArchive| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: [[NewsArchive/2011| 2011]] :: '''2010''' :: [[NewsArchive/2009| 2009]] :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]

=2010=
==October ==
[[image:NAMIC-BWH-PR.png|left|400px|thumb|'''NIH renews funds for the National Alliance for Medical Imaging Computing''' The National Institutes of Health (NIH) has renewed funding for the National Alliance for Medical Imaging Computing (NA-MIC) for the next four years. NA-MIC, under the leadership of '''Ron Kikinis, MD''', Director of the Surgical Planning Laboratory at Brigham and Women’s Hospital, has been granted $15.8 million through the NIH Roadmap Initiative for medical research. [http://www.brighamandwomens.org/about_bwh/publicaffairs/news/pressreleases/PressRelease.aspx?PageID=749 Read more...]]]
==September==
[[image:MICCAI_logo.jpg|left|400px|thumb|'''MICCAI 2010 Tutorial''' This tutorial provides insights on practical approaches for bridging the gap between the scientific advances made by the biomedical imaging community and their widespread use in the clinical research community. By the end of the day course participants will know how to use the NA-MIC kit to facilitate greater use of their own algorithms by clinical end users. The tutorial was held on Monday September 20, 2010 from 8:45 am to 6:00 pm at the China National Convention Center (CNCC), No.7 Tianchen East Road, Chaoyang District, Beijing, China. [http://www.na-mic.org/Wiki/index.php/MICCAI_2010 Read more...]]]
==June==
[[image:PW-MIT2010.png|left|400px|thumb|'''2010 Summer Project Week''' The 11th PROJECT WEEK of hands-on research and development activity will be hosted at MIT from June 21 to June 25, 2010. Participants will engage in open source programming using the NA-MIC Kit, algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational biomedical research deliverables of the sponsoring centers and their collaborators. [http://www.na-mic.org/Wiki/index.php/2010_Summer_Project_Week Read more...]]]

==April==
[[image:Iowa-slicertraining.png|left|400px|thumb|'''3D Slicer Training Event''' The National Alliance for Medical Image Computing (NA-MIC) is sponsoring a Slicer3 training workshop at the University of
Iowa on April 9 and 10, 2010. The format for the workshop will consist of a large presentation on Friday afternoon, and two sessions on Saturday including a Programming Tutorial session and End User training.
[http://www.na-mic.org/Wiki/index.php/2010_University.of.Iowa Read more...]]]

==March==
[[image:NCIGT-WS2010.png|left|400px|thumb|'''Image-Guided Therapy Workshop''' The 3rd Annual Image-Guided Therapy Workshop is sponsored by the National Center for Image-Guided Therapy (NCIGT) and the National Institute of Health (NIH). This workshop will be focused on Multimodal Imaging in the Operating Room. The goal is to learn about state-of-the-art and future trends in operating suites that use more than one imaging modality and serve more than one clinical speciality. March 8-9, 2010, Washington DC. [http://www.ncigt.org/pages/IGT_Workshop_2010 Read more...]]]

==January==
[[image:PW-SLC2010.png|left|400px|thumb|'''2010 Winter Project Week''' The tenth working research PROJECT EVENT, jointly sponsored by [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst] and [http://www.cimit.org CIMIT], will be hosted in Salt Lake City, Utah, from January 4-8, 2010. This will be held in conjunction with the NA-MIC [http://www.na-mic.org/Wiki/index.php/AHM_2010 all hands meeting (AHM)] on January 7th. [http://www.na-mic.org/Wiki/index.php/AHM_2010 Read more...]]]

NewsArchive/

2016-06-29T17:30:00Z

Zack: Update from Wiki

NewsArchive/2011

2016-06-29T17:29:59Z

Zack: Update from Wiki

__NOTOC__
=News Archive=
[[NewsArchive| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: '''2011''' :: [[NewsArchive/2010| 2010]] :: [[NewsArchive/2009| 2009]] :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]
=2011=
==November==
[[Image:RSNA2011.png|left|400px|thumb|'''RSNA 2011''' The 97th Annual Meeting of the Radiological Society of North America was held from November 27 - December 2, 2011 in Chicago IL. [http://rsna2011.rsna.org Read more...]]]
==June==
[[image:PW-MIT2011a.png|left|400px|thumb|'''2011 Summer Project Week''' We are pleased to announce the 13th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the NA-MIC Kit, algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The 13th Project Event is scheduled for June 20-24 at MIT. [http://wiki.na-mic.org/Wiki/index.php/2011_Summer_Project_Week Read more...]]]

==March==
[[image:Slicer3-6Announcement-v2.png|left|400px|thumb|'''New Slicer3.6.3 Release''' After a few months of testing and debugging, the slicer team is proud to announce the release of 3D Slicer version 3.6.3. This release includes over 50 bug fixes by over 20 developers at about a dozen companies and institutions -- the most comprehensive slicer release to date.
Binaries for the 3.6.3 release are available at the [http://www.slicer.org/pages/Special:SlicerDownloads Slicer Download] page.
[http://www.slicer.org/slicerWiki/index.php/Slicer3:3.6_Final_Issues#DONE:_Issues_fixed_for_inclusion_in_Slicer_3.6.3_--_included_in_release_builds_of_March_4.2C_2011 Read more...]]]

NewsArchive/2009

2016-06-29T17:29:58Z

Zack: Update from Wiki

__NOTOC__

[[NewsArchive| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: [[NewsArchive/2011| 2011]] :: [[NewsArchive/2010| 2010]] :: '''2009''' :: [[NewsArchive/2008| 2008]] :: [[NewsArchive/2007| 2007]]

=2009=
==September==
[[Image:MICCAIAward.png|left|400px|thumb|'''MICCAI 2009 Young Scientist Award''' Each year, the MICCAI conference presents a number of awards to graduate student and early career scientists for outstanding papers published in the MICCAI proceedings. For MICCAI 2009, the Young Scientist Awards winner in the Medical Image Computing: Segmentation and Analysis category was [http://www.na-mic.org/publications/item/view/1696 Joint Segmentation of Image Ensembles via Latent Atlases] by Tammy Riklin Raviv, Koen Van Leemput, William M. Wells III, Polina Golland. In the Medical Image Computing: Shape Analysis category the first prize went to [http://www.na-mic.org/publications/item/view/1697 Local White Matter Geometry Indices from Diffusion Tensor Gradients] by Peter Savadjiev, Gordon Kindlmann, Sylvain Bouix, Martha E. Shenton, Carl-Fredrik Westin. [http://www.miccai2009.org Read more...]]]

==May==
[[Image:Slicer3.4-cover-c.jpg|left|400px|thumb|'''Slicer 3.4 is released''' The community of Slicer developers is proud to announce the official release of Slicer 3.4 as of May 2009.
Slicer 3.4 is a general purpose biomedical computing application with extensive built-in visualization and analysis capabilities, accessible through an easy to use graphical interface. [http://www.slicer.org/slicerWiki/index.php/Announcements:Slicer3.4 Read more...]]]

==January==
[[Image:Arctic.png|left|400px|thumb|'''Slicer Tutorial Contest January 2009''' As part of the NA-MIC Training Core activities we are building a portfolio of tutorials for the basic functions and functionalities available in Slicer. The primary purpose of this tutorial contest is to enrich the training materials that are available to end-users and developers using 3D Slicer. The first prize of the AHM 2009 contest has been awarded to the [http://www.na-mic.org/Wiki/index.php/UNC_ARCTIC_Tutorial ARCTIC Slicer3] tutorial (UNC); the second prize has been given to the Non-human primates segmentation tutorial (Virginia Tech); the third prize has been awarded to the Prostate Therapy Planning Tutorial (BWH-U.Toronto). [http://www.na-mic.org/Wiki/index.php/Events:TutorialContestJan2009 Read more...]]]

NewsArchive/2008

2016-06-29T17:29:57Z

Zack: Update from Wiki

__NOTOC__

[[NewsArchive| 2014]] :: [[NewsArchive/2013| 2013]] :: [[NewsArchive/2012| 2012]] :: [[NewsArchive/2011| 2011]] :: [[NewsArchive/2010| 2010]] :: [[NewsArchive/2009| 2009]] :: '''2008''' :: [[NewsArchive/2007| 2007]]

=2008=
==September==
[[Image:Logo_MICCAI2008.png|left|400px|thumb|'''MICCAI 2008 "Interfacing Third-party Software with the NA-MIC Open-source Toolkit" Workshop''' The workshop combines introductory lectures on the software components of the NA-MIC kit, with hands-on tutorial sessions that guide the participants through the integration of the open-source tools with third-party software. Participants will be able to interface their own algorithms with the NA-MIC kit to facilitate greater interoperability of advanced medical image analysis software tools. This course is intended for scientists and engineers of the medical image analysis community. The event is held on Wednesday, September 10, 2008 at New-York City University, NY. [http://www.na-mic.org/Wiki/index.php/Miccai_2008_Tutorial Read more...]]]

==September==
[[Image:NA-MIC_Workshop_Stanford_09-03-2008.PNG|left|400px|thumb|'''Stanford 2008 Slicer Workshop''' This workshop is an introduction to the Slicer3 software platform, part of the NA-MIC open-source toolkit and provides a 1/2 day end-user session geared for all levels, followed by a 1/2 day programming session geared for developers. The event is held on Wednesday September 3, 2008 at Stanford University Medical Center. [http://www.na-mic.org/Wiki/index.php/Stanford_2008_Slicer_Workshop Read more...]]]

==August==
[[Image:2008NCBCAHM.JPG|left|400px|thumb|'''2008 NCBC All Hands Meeting''' The National Centers for Biomedical Computing (NCBCs) are cooperative agreement awards that are funded under the NIH Roadmap for Bioinformatics and Computational Biology. The All Hands Meeting is held on August 13-14, 2008 at the Natcher Conference Center in Bethesda, Maryland. [http://meetings.nigms.nih.gov/index.cfm?event=home&ID=4095 Read more...]]]

==June==
[[Image:ProjectWeek-2008.png|400px|left|thumb|'''2008 Summer Project Week''' The project week is a biannual open source programming event that combines hands-on projects using the NA-MIC methodology and the NA-MIC toolkit with breakout sessions on advanced medical image analysis topics. This event is the seventh of the series. The participants meet during the week of June 23-27, 2008, at MIT. [http://www.na-mic.org/Wiki/index.php/2008_Summer_Project_Week Read more...]]]

==January==
[[Image:SLC-crop.jpg|left|400px|thumb|'''AHM 2008''' NA-MIC participants meet for a all-hands meeting (AHM) in Salt Lake City, UT. The combined AHM, EAB and Project Week will be held during the week of January 7-11, 2008. [http://wiki.na-mic.org/Wiki/index.php/AHM_2008 Read more...]]]

DBP:Lupus

2016-02-09T04:33:02Z

Zack: Update from Wiki

=Lupus Solutions=
{|class="wikitable"
|[[Image:Scully-FrontHumNeurosci2010-fig3.jpeg|600px]]
|
*'''Data''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Brain: White Matter Lesions for Lupus Study]
*'''Tutorial''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials Classification of White Matter lesions for Lupus]
*'''Software for Slicer 3.6''' [http://www.slicer.org/slicerWiki/index.php/Modules:LesionSegmentationApplications-Documentation-3.6 White Matter Lesion Segmentation in Lupus]
*'''Representative Publication''' [http://www.na-mic.org/publications/item/view/1837 1837]
*'''Wikinotes''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND MIND Institute Wiki]
|}
===Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosus===

Drs. Jeremy Bockholt and Charles Gasparovi of the MIND Institute and the University of New Mexico are investigating the etymology of brain lesions in neuropsychiatric systemic lupus erythematosus (NPSLE). Fatal NPSLE is accompanied by small focal white matter lesions in the brain, among other findings. Making accurate measurements of the location, size, and time course of these lesions is a critical analytical step. However, brain lesions in lupus vary in MRI intensity and temporal evolution. Moreover, the case under consideration may be acute, chronic, or resolving. These factors add to the complexity of white matter lesion analysis in NPSLE. The primary goal of this DBP is to create an end-to-end solution that permits individual analysis of white matter lesions. Clinical investigators from this DBP (Drs. Roldan and Sibbitt) have provided a dataset of 30 brain scans from 25 patients with qualitative evidence of small focal white matter lesions. NA-MIC has the technology and expertise to provide a turnkey lesion segmentation solution for white matter analysis. In collaboration with the Computer Science Core, the PIs from the MIND Institute and University of New Mexico have developed a novel solution for automated lesion analysis capable of segmenting white matter lesions for size, location, and tissue intensity as well as monitoring the course of individual lesions over time.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

DBP:HD

2016-02-09T04:33:01Z

Zack: Update from Wiki

__NOTOC__
=HUNTINGTON’S DISEASE=
'''PI: Hans Johnson, Iowa University'''

==Specific Aims==
The NIH-funded project “Neurobiological Predictors of Huntington’s Disease” (PREDICT-HD) is studying Huntington’s disease (HD), a neurodegenerative genetic disorder that affects muscle coordination, behavior, and cognitive function, and causes severe debilitating symptoms by middle age. The aims of this DBP capitalize on two unique aspects of HD among neurodegenerative disorders, namely, the ability to know in advance exactly who will develop the disease and the knowledge that all affected individuals have the same root cause (i.e., a CAG repeat expansion in the huntingtin gene).

'''1. Perform individualized longitudinal shape change quantification from multimodal data.'''
Morphometric brain differences begin 15 years or more before the symptoms of HD become debilitating. The ultimate goal of the PREDICT-HD study is to sufficiently define the neurobiological progression of HD in at-risk individuals so that clinical trials of potential disease-modifying therapies can be performed before symptoms reach a debilitating stage. Previous cross-sectional group analyses show that just before patients manifest symptoms, the caudate and putamen are severely affected [1] and there are widespread changes in cortical thickness [2, 3]. The NA-MIC multi-subject single modality group-wise registration methods will be modified to perform single subject multimodality longitudinal registrations.

'''2. Perform full brain diffusion tensor imaging tractography analysis.'''
In addition to morphometric gray matter measurements, diffusion tensor imaging (DTI) fractional anisotropy measurements indicate that white matter changes occur very early in HD [3-5]. The development of tools for DTI data hold great promise for identifying early disease markers suitable for measuring longitudinal trajectory changes over short time intervals. Tools for segmenting white matter based on the high-resolution 3T multimodal scans plus DTI data consistently identify white matter anatomical regions. Methods for white matter
fiber tracking from DTI data can identify anatomically connected regions within a single subject. Longitudinal analysis of white matter changes will help identify cause/effect relationships of disease progression between cortex, white matter, and sub-cortical connected regions.

'''3. Deploy extensible tools for sharing source data, derived data, algorithms, and methods to multi-site analysis teams.''' It is widely recognized that the PREDICT-HD imaging dataset has extraordinary value. It is further enriched through quality assurance documentation and integration with clinical measures. There is a great need to deploy mechanisms that facilitate external collaboration by providing: (1) data transfer methods for both raw scanner data and derived data, (2) access and interfaces to a wide variety of inter-connected image-processing
algorithms from other institutions, (3) procedures for integration of externally generated derived datasets with the central repository, and (4) training material on how to best use the datasets.

==Background==
The PREDICT-HD (5 R01 NS04006) study is an international 30-site observational study of longitudinal neurodegeneration of persons at-risk for HD with continuous funding from 2001 to 2013. PREDICT-HD has fulfilled all aims from its initial award and has become part of a world-wide effort to provide treatments for HD, both symptomatic and presymptomatic (“premanifest”). The PREDICT-HD cohort and database have become international resources and offer an unprecedented opportunity to examine the pathophysiology and neurobiology of early HD. The specific short-term aims of PREDICT-HD are: (1) to refine the prediction of disease
diagnosis (motor conversion) using longitudinal measures of plasma, imaging, cognitive performances, motor ratings, and psychiatric measures, and (2) to identify and characterize the natural history of sensitive markers of disease onset and progression that become abnormal prior to clinical diagnosis.

[[Image:DBP.HD1.png|500px|left|thumb|Figure 1. '''A, B''' Relationship between estimated years to diagnosis of Huntington's disease and motor exam score and striatal volumes. '''C''' Distribution of age of onset for individuals with 36-56 CAG repeats based on the parametric model. ''Red'' indicates most likely time of diagnosis. ''Blue'' line is porposed time period when interventional therapies would have greatest impact.]]The curves in panels A and B of Figure 1 show a cross-sectional analysis of HD subjects, where the red boxes represent the most likely time of neurological diagnosis and the blue boxes represent the proposed window for starting a disease-modifying intervention [6]. A well-established parametric survival model [7], based on CAG repeat length, predicts the probability of observed debilitating motor neurological symptoms. This is the current basis for disease onset at different ages of individual patients. A graphical depiction of this “onset” model is shown in Figure 1C, where the red line indicates the most likely age at which a neurological diagnosis will be made.

The cross-sectional analyses that led to the results shown in Figure 1 are informative for identifying the general progression of disease, but individualized longitudinal analysis is needed to identify the appropriate time to start interventional treatment in the individual patient. The focus of this proposal is to improve the longitudinal analysis methods we currently use to include informed intervention criteria suitable for application decades before debilitating neurological symptoms manifest.

The PREDICT-HD study is currently collaborating with pharmaceutical companies to develop a promising long-term (3-10 year) therapeutic treatment for HD that involves a permanent implantable infusion pump for drug delivery. The NA-MIC Kit will contribute several technical aspects: (1) Precise subject-specific morphological mapping of white matter and gray matter sub-cortical regions will assist in developing the necessary masstransport models needed to optimize pump placement. (2) Longitudinal analysis of a single subject will inform clinicians of the most appropriate time for clinical intervention. (3) Finally, precision anatomical labeling will aid surgical planning for implantation.

==Investigators==

{|class="wikitable"
|-
|'''NAME'''
|'''DEGREE'''
|'''INSTITUTION'''
|'''EXPERIENCE'''
|'''ROLE'''
|-
|[http://www.psychiatry.uiowa.edu/mhcrc/IPLpages/IPL_postdoc.html HANS JOHNSON]
|PH.D.
|UNIVERSITY OF IOWA
|MEDICAL IMAGE PROCESSING, COMPUTER ENGINEERING & IMAGING INFORMATICS
|DBP PI
|-
|[http://www.uihealthcare.com/depts/huntingtonsdisease/staff/janepaulsen.html JANE S. PAULSEN]
|PH.D.
|UNIVERSITY OF IOWA
|NEUROPSYCHOLOGIST
|PREDICT-HD PI
|-
|[http://www.medicine.uiowa.edu/Radiology/faculty-staff/faculty/magnotta-vincent.html VINCENT MAGNOTTA]
|PH.D.
|UNIVERSITY OF IOWA
|DIFFUSION TENSOR IMAGING, RADIOLOGY
|CONSULTANT FOR DTI PROCESSING
|-
|KENT WILLIAMS
|M.S.
|UNIVERSITY OF IOWA
|SOFTWARE ENGINEERING, NA-MIC DEVELOPMENT BEST PRACTICES
|DBP SOFTWARE ENGINEER
|-
|[http://www.mir.wustl.edu/research/physician2.asp?PhysNum=45 DAN MARCUS]
|PH.D.
|WASHINGTON UNIVERSITY, ST. LOUIS
|NEUROIMAGING INFORMATICS
|LEAD INFORMATICS & DATA DISSEMINATION
|-
|[http://www.cs.unc.edu/~styner/ MARTIN STYNER]
|PH.D.
|UNIVERSITY OF NORTH CAROLINA
|COMPUTER SCIENCE
|LEAD ALGORITHM & ENGINEERING CONTACT
|}

==Methods==

'''Aim 1.''' To analyze longitudinal shape change, we will use a set of 80 subjects with between 3 and 6 longitudinal multimodal image sets (T1/T2/PD) and manually validated caudate, putamen, and thalamus segmentations to perform longitudinal registrations (i.e., not pairwise). This NA-MIC method will include provisions for addressing missing modalities. The proposed analysis will be used to provide robust estimates of longitudinal change. Additionally, the current NA-MIC shape analysis tools will be used to analyze a set of 225 subjects with between 3 and 6 T1 only scans and manually validated sub-cortical segmentations. These shape analyses will be used to create a normative model. In this way, changes in an individual’s scores can be used to inform clinical counseling and intervention scheduling decades before a neurological motor diagnosis is made.

The NA-MIC Kit 3D Slicer “Change Tracker” wizard provides functionality for investigating small longitudinal changes in pairwise bright object meningioma growth analysis. The “Change Tracker” is an exceptional prototype for the family of tools needed to analyze gray matter subcortical structure atrophy rates as a disease state marker. Development of the “Change Tracker” tools will be performed to generalize the tool for monitoring changes to subcortical brain structures.

'''Aim 2.''' The development of DTI quality control and atlas-building tools suitable for longitudinal white matter change quantification will be greatly accelerated by leveraging the existing NA-MIC expertise in DTI data processing. An HD-specific atlas will be constructed from a set 25 subjects with longitudinal 3T imaging data containing 2 high resolution T1, a high resolution T2, a 32 direction DTI sequence, and manually validated brain segmentations. We will collaborate with NA-MIC developers to create tools for a longitudinal analysis
pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. We will use the same data for whole brain longitudinal analysis of the DTI connectivity using stochastic tractography tools for network and pathology detection. Customized user interfaces will extend the diffusion tractography visualization modules in the 3D Slicer to target reporting of longitudinal fiber tracking results to the clinical audience.

'''Aim 3.''' To accomplish the dissemination and collaboration goals, we will deploy the XNAT environment. This effort will include quality-assurance procedures, data-processing tools, and a common knowledge base accessible to the extended HD community of image-processing experts. The NA-MIC data-sharing tools will be extended to facilitate the dissemination of raw scan data, derived image datasets, and measurement scores for Aims 1 and 2. The existing morphometric analysis pipelines used to create the manually validated segmentations
also will be incorporated into the XNAT processing pipeline.

==Connections between this work and any of the other 3 proposed DBPs==
The University of Iowa investigators have been long-term active participants in the development process for several NA-MIC Kit resources including ITK, XNAT, and 3D Slicer. The specific development needs of this DBP are consistent with many of the needs stated in the other DBPs. All of the projects have a need for robust well documented standardized workflows, longitudinal registration, and robust segmentation of anatomical regions. The HD, TBI, and RT for head and neck cancer DBPs share a common need for sensitive shape change measurement from longitudinal scans. Finally, the HD and TBI DBPs have a need for improved multi-model data analysis including quality control and analysis tools for investigating longitudinal white matter changes from diffusion tensor imaging.

==Deliverables, Timeline, Impact==
{|class="wikitable"
|-
|'''SPECIFIC AIMS'''
|'''Year 1'''
|'''Year 2'''
|'''Year 3'''
|-
|'''Aim 1'''
|Preliminary tools for longitudinal shape change applied to existing sets of segmented subcortical structures
|Improve shape analysis tools and apply to larger cohort with multiple study visits
|Create normative models of shape change in healthy aging and disease (HD)
|-
|'''Aim 2'''
||Quality control pipeline of DTI datasets. Test with preliminary tool for longitudinal analysis of white matter tracts
|Put refined tool for longitudinal analysis of fiber tracts into workflow.
Application to subjects with apparent pathologic changes
|Develop workflow of optimized longitudinal white matter analysis for whole brain tractography
|-
|'''Aim 3'''
|Stand up XNAT instance for PREDICT-HD project, customize to manage all expected data types.
Import existing data for internal use and testing
|Develop and incorporate PREDICT-HD specific workflows developed in Aims 1 and 2 into XNAT
|Documentation of workflows with training materials. Enable sharing with the scientific community as dictated by the PREDICT-HD project.
|}

The Iowa investigators will collaborate with the Computer Science Core to develop new and refine existing tools to achieve the specific aims of the HD-DBP. This effort will include participation in the two All-Hands Meetings each year, as well as generation of presentations for scientific conferences and peer-reviewed publications. When mutually beneficial, tool development will be coordinated with other DBPs. Anonymized versions of the imaging datasets necessary to achieve the stated aims of this DBP will be made available to the entire NA-MIC community to facilitate algorithm, workflow, training, and documentation efforts. The developed tools will follow the software engineering Best Practices guidelines established by NA-MIC and will be made available through the Service and Dissemination Cores as well as the [http://www.nitrc.org Neuroimaging Informatics Tools and Resources Clearinghouse]. In the second and third years of the project, training events will be held to coincide with the [http://www.euro-hd.net Annual European Huntington’s Disease Network Meeting], and the [http://www.humanbrainmapping.org Annual Human Brain Mapping Conference] to expose the larger HD community to the NA-MIC resources. An XNAT instance will be deployed with the entire deidentified PREDICT-HD imaging dataset, representative standard workflows, and links to the clinical variables in the [http://www.ncbi.nlm.nih.gov/gap dbGaP] will be made available to external researchers through a standard approval process. The NA-MIC efforts will facilitate our needs to: (1) integrate data from multiple protocols for generating a set of measures that can be studied to explore the longitudinal inter-relationships between known areas of degeneration, (2) disseminate a well documented set of best practices and training events for the HD imaging community to empower collaboration, (3) deploy a common centralized data-sharing infrastructure (i.e., XNAT) that is well integrated with the training software and development practices necessary to gain access to new imaging methodologies.

==References==
#Paulsen JS, Magnotta VA, Mikos AE, Paulson HL, Penziner E, Andreasen NC, et al. Brain structure in preclinical Huntington’s disease. Biol Psychiatry. 2006;59(1):57-6. PMID: 16112655.
#Nopoulos P, Magnotta VA, Mikos A, Paulson H, Andreasen NC, Paulsen JS. Morphology of the cerebral cortex in preclinical Huntington’s disease. Am J Psychiatry. 2007;164(9):1428-34. PMID: 17728429.
#Rosas HD, Hevelone ND, Zaleta AK, Greve DN, Salat DH, Fischl B. Regional cortical thinning in preclinical Huntington disease and its relationship to cognition. Neurology. 2005;65(5):745-7. PMID: 16157910.
#Reading SA, Yassa MA, Bakker A, Dziorny AC, Gourley LM, Yallapragada V, et al. Regional white matter change in pre-symptomatic Huntington’s disease: a diffusion tensor imaging study. Psychiatry Res. 2005;140(1):55-62. PMID: 16199141.
#Beglinger LJ, Nopoulos PC, Jorge RE, Langbehn DR, Mikos AE, Moser DJ, et al. White matter volume and cognitive dysfunction in early Huntington’s disease. Cogn Behav Neurol. 2005;18(2):102-7. PMID:15970729.
#Paulsen JS, Langbehn DR, Stout JC, Aylward E, Ross C, A, Nance M, et al. Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J Neurol Neurosurg Psychiatry. 2008;79:874-80. PMID: 18096682.
#Langbehn DR, Brinkman RR, Falush D, Paulsen JS, Hayden MR. A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length. Clin Genet. 2004;65(4):267-77. PMID: 15025718.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

DBP:Overview

2016-02-09T04:33:00Z

Zack: Update from Wiki

__NOTOC__
=Driving Biological Projects 2010-2013=

The role of the driving biological projects (DBPs) is to motivate innovation by
providing data and clear targets to drive algorithm and software development by the Computer Science Core. These projects must represent important practical problems that have a broad impact on health care delivery. The overall goal of NA-MIC, as it relates to the issue of personalized medicine, is to create integrated representations of the human body in health and disease that contribute to the overall understanding of each patient and each treatment decision. Medical image computing already has assumed a central role in the standard of care across a wide range of clinical indications. Yet, significant gaps remain between what is technically feasible versus what is practical. The current DBPs span a range of organ systems and medical specialties, including both degenerative and traumatic conditions. They bring substantial problems of a subject-specific nature that can be solved by improved image analysis. As a consequence, a strong core of general purpose technology will emerge from the union of the specific development paths of each of the DBPs.

{|class="wikitable"
|[[image:DBP.AF1-c.png|120px|link=DBP:Atrial_Fibrillation]]
|[[DBP:Atrial Fibrillation|'''Atrial Fibrillation''']] The interventional cardiology application (CARMA) for the treatment of atrial fibrillation (AF), which is currently in development at the University of Utah, requires an integrated suite of software tools optimized for cardiac MRI. The purpose of these tools is to extract meaningful information to guide case management and treatment from customized acquisition sequences within the time constraints of an interventional procedure (i.e., 30 minutes). Using automated segmentation for precision guidance of interventional therapy based on the integration of pre-procedural and intra-procedural image data, these tools will enable targeted radiofrequency ablation of the portion of the diseased heart tissue that produces the arrhythmia.
|-
|[[image:DBP.HD1-c.png|120px|link=DBP:HD]]
|[[DBP:HD|'''Huntington's Disease''']] The multi-site PREDICT-HD consortium led by the University of Iowa on early detection of Huntington’s Disease (HD) uses multimodal image, genetic, and clinical data from a large population to formulate and test hypotheses about the evolution of chronic diseases in at-risk individuals. Early intervention with implantable drug delivery devices could revolutionize the treatment of HD, but there are attendant risks. The statistical models that result from the application of customized image analysis to the PREDICT-HD cohort will provide a basis for conducting clinical pharmaceutical trials with increased sensitivity both to improvements and adverse outcomes.
|-
|[[image:DBP.HNC1-c.png|120px|link=DBP:Head and Neck Cancer]]
|[[DBP:Head and Neck Cancer|'''Head and Neck Cancer''']] The project in adaptive radiotherapy (RT) at Massachusetts General Hospital requires the quantification of change in body systems during disease progression and management to guide the application of radiation to tumor volumes while sparing critical structures. Segmentation and registration of serial CT datasets and interaction with commercial treatment planning systems will be used to determine best practices for radiotherapy, in general, with a particular benefit anticipated for proton therapy.
|-
|[[image:DBP.TBI-c.png|120px|link=DBP:TBI]]
|[[DBP:TBI|'''Traumatic Brain Injury''']] Current neuroimaging technologies are not able to detect neuroanatomical changes in individuals suffering from trauma or other pathology, because current methods rely on spatial normalization across subjects. However, longitudinal imaging allows patients to serve as their own controls and removes the need for inter-subject spatial alignment. This collaboration with UCLA to study traumatic brain injury (TBI) will take advantage of longitudinal imaging analysis by delivering customized analysis pipelines that are robust in the presence of the case-specific imaging signatures of head trauma. These pipelines promise to reveal previously hidden consequences of TBI to inform clinical decision-making. The breadth of the topics addressed by this portfolio of clinical research, together with the diverse set of applications targeted by the NA-MIC collaboration efforts described under Dissemination demonstrate the range of health care issues that can benefit from enhanced computer imaging systems.
|}

DBP:Schizophrenia

2016-02-09T04:32:58Z

Zack: Update from Wiki

=Schizophrenia Solutions=
{|class="wikitable"
|[[Image:FIG.2-9.arcuate.png|400px]]
||
*'''Data''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Multimodality (sMRI, fMRI, DTI) Brain from Schizophrenia Study]
*'''Tutorial''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials Stochastic Tractography to extract, visualize and quantify white matter connections from Diffusion Images in Schizophrenia Study]
*'''Software for Slicer 3.6''' [http://www.slicer.org/slicerWiki/index.php/Modules:StochasticTractography-Documentation-3.6 Stochastic Tractography]
*'''Representative Publications''' [http://www.na-mic.org/publications/item/view/1481 1481] | [http://www.na-mic.org/publications/item/view/1763 1763] | [http://www.na-mic.org/publications/item/view/1531 1531] | [http://www.na-mic.org/publications/item/view/1574 1574] | [http://www.na-mic.org/publications/item/view/1690 1690] | [http://www.na-mic.org/publications/item/view/1819 1819] | [http://www.na-mic.org/publications/item/view/1821 1821] | [http://www.na-mic.org/publications/item/view/1845 1845]
*'''Wikinotes''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard NA-MIC Wiki]
|}
===Velocardiofacial Syndrome (VCFS) - A Genetic Model for Schizophrenia===

The Schizophrenia DBP, led by Dr. Marek Kubicki at Harvard Medical School, concerns the nature and anatomy of brain changes associated with schizophrenia and the genetically related VCFS, both believed to be neurodevelopmental disorders. Patients with VCFS have a deletion in chromosome 22. VCSF shares in common with schizophrenia several specific genes. VCFS also is the most important prognostic factor in schizophrenia hereditability, since 30% of patients with VCFS will eventually develop a psychosis. Dr. Kubicki has acquired a dataset of brain scans from patients with schizophrenia and VCSF. He is using VCSF as a model for schizophrenia by comparing similarities and differences in the anatomical connections between the language centers of the human brain. The language centers are the most functionally affected domains in schizophrenia. These comparisons require tools and methods beyond the capabilities of standard imaging technologies, but attainable through NA-MIC algorithms and methodologies (Structural MRI (sMRI), Functional MRI (fMRI), Diffusion Tensor Imaging (DTI)). The tools developed for this dataset over the past several years have enabled the delineation and measurement of white matter connections individually, both in schizophrenia and in VCFS. The results of these studies have reinforced the notion that white matter changes in schizophrenia are widespread and neurodevelopmental in origin. On the basis of this research, improved algorithms for early prediction, detection, and treatment of schizophrenia should be forthcoming.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

DBP:Head and Neck Cancer

2016-02-09T04:32:57Z

Zack: Update from Wiki

__NOTOC__
=ADAPTIVE RADIOTHERAPY FOR HEAD AND NECK CANCER=
'''PI: Greg Sharp, MGH'''

Head and neck cancers account for about 60,000 new cancer cases per year and represent about 6%
of all cancers in the United States [1]. These cancers are treated by a combination of chemotherapy, radiotherapy, and surgery. The five-year survival is approximately 50%. During a six-week regimen of radiotherapy, head and neck cancer patients often exhibit anatomic changes that affect their treatment. These changes include tumor regression or growth, changes in lymph node size, and changes in air cavities. Uncorrected, these changes can increase the risk of treatment complications or reduce treatment efficacy.

Adaptive radiotherapy addresses the problem of anatomic change by incrementally adjusting the radiotherapy plan, and is a prime example of personalized medicine. A mid-treatment adjustment is complex: it requires a new CT image, image segmentation, deformable registration, and mapping of the previously delivered dose onto the new image. This project proposes to use the NA-MIC Kit to develop a simple, practical workflow for achieving adaptive radiotherapy which can be applied on a case-by-case basis.

==Specific Aims==
'''1. Develop an open computational workflow for adaptive radiotherapy.'''
We will develop a practical workflow for adaptive therapy planning that enables the registration of successive CT scans, segmentation of the tumor and the critical structures in CT, mapping of prior radiation plans onto new images, and planning of additional radiation therapy. We hypothesize that a flexible framework or workflow will enable an adaptive plan to be generated, reviewed, and ready for use within hours of acquisition.

'''2. Validate the accuracy of image analysis algorithms for radiotherapy.'''
We will investigate and quantify the accuracy of automatic image registration and segmentation algorithms to establish spatial correspondences across consecutive CT scans and to delineate structures for radiation planning. We will adapt and compare the algorithms within NA-MIC Kit, and work with the Computer Science Core to develop novel segmentation and registration methods tailored to adaptive radiotherapy planning.

'''3. Evaluate the dosimetric gain of adaptive radiotherapy.'''
Using CT images acquired from patients before treatment and at the mid-point during treatment, we will perform dosimetric comparisons of traditional radiotherapy and adaptive radiotherapy. We hypothesize that adaptive radiotherapy will result in a clinical gain in the probability of tumor control and/or a reduction in complication rate, as predicted by radiation dose-response models.

This project features three innovations: (1) It will establish the feasibility of an open-source software platform for adaptive radiotherapy; (2) optimize the platform for radiotherapy; and (3) evaluate the clinical gain through dosimetric comparison.
Our aims support the NIH-funded project “Proton radiation therapy research” (2 P01 CA21239-29A,
DeLaney PI) by providing image analysis tools for adaptive proton therapy. The clinical hypothesis of the supported project is that proton-beam radiotherapy improves the therapeutic ratio between cure probability and complication risk in non-small cell lung cancer, liver tumors, pediatric medulloblastoma and rhabdomyosarcoma, spine/skull base sarcomas, and paranasal sinus malignancies. NA-MIC will play a critical role in the project
by bringing adaptive radiotherapy to proton treatments of the paranasal sinus. NA-MIC is uniquely positioned to provide user-ready software and state-of-the-art imaging algorithms.

==Background==
Anatomic changes during radiotherapy can cause the clinical target volume to move outside of the previously planned treatment field, or conversely, can cause critical structures such as the parotid gland, spinal cord, and brainstem to move into the treatment field. Adaptive radiotherapy has been explored for photon therapy [2,3], but the technique is not yet widely used. The need for adaptive radiotherapy is gaining in importance as the use of more aggressive chemotherapy increases [4]. Adaptive radiotherapy, may be especially
important for proton therapy. Protons have a finite range of penetration and treatment accuracy depends upon both lateral positioning and target depth.

[[Image:DBP.HNC1.png|600px|left|thumb|Figure 1. Proton treatment of the nasal cavity. In the original plan (left), we see uniform coverage of the target with good sparing of the retina and optic nerves. At mid-treatment (right), changes in the nasal cavity cause a non-uniform dose to the target and a higher dose to optic structures.]] Figure 1 shows that the original treatment plan (left) has a uniform target distribution and good sparing of critical organs. At mid-treatment (right), there is an anatomic shift, which results in undesirable hot spots and a high dose to the right optic nerve and retina. To date, there have been no studies on the effect of anatomic change for proton-beam treatments in the head and neck. This study will be the first to assess whether plan adaptation can improve proton treatments for patients.

One of the reasons that adaptive radiotherapy has not become widespread is that dose planning
requires expert training and sophisticated software. The additional treatment planning required to account for anatomical changes is considered too costly in terms of clinical time. Automated adaptation of the plan promises to alleviate this problem, but current systems still require signifi cant time investment per case, making it difficult to evaluate the impact of different algorithmic strategies. The NA-MIC Kit will serve a critical role in making adaptive therapy a practical reality. 3D Slicer will be used as the imaging platform for registration and segmentation of longitudinal CT images, as well as for clinical visualization and review.

==Investigators==
{|class="wikitable"
|-
|'''NAME'''
|'''DEGREE'''
|'''INSTITUTION'''
|'''EXPERIENCE'''
|'''ROLE'''
|-
|[http://gray.mgh.harvard.edu/index.php?option=com_content&view=article&id=9:gregory-c-sharp-phd&catid=1:faculty&Itemid=8 GREG SHARP]
|PH.D.
|MGH
|MEDICAL PHYSICIST
|DBP PI
|-
|[http://www.dfhcc.harvard.edu/membership/profile/member/1082/0/ ANNIE CHAN]
|M.D.
|MGH
|RADIATION ONCOLOGIST
|CLINICAL EVALUATION
|-
|[http://sites.google.com/a/isomics.com/www/ STEVE PIEPER]
|PH.D.
|ISOMICS
|SOFTWARE ENGINEERING
|LEADING ENGINEERING CONTACT
|-
|[http://people.csail.mit.edu/polina/ POLINA GOLLAND]
|PH.D.
|MIT
|COMPUTER SCIENTIST
|LEAD ALGORITHMS CONTACT
|}

==Methods==
[[Image:DBP.HNC2.png|500px|thumb|left|Figure 2. Adaptive radiotherapy with multiple CT scans.]]

'''Aim 1.''' We will define a workflow for adaptive radiotherapy that uses the NA-MIC Kit together with existing commercial treatment planning software (TPS). TPS will be used to create the initial treatment plan. Using one or more additional CT scans acquired during the treatment course, as defined by existing clinical protocols (see Figure 2), the NA-MIC Kit will provide image registration and segmentation. Registration and segmentation results will be transferred back to the TPS for beam definition and dose calculations. We will then use NA-MIC visualization tools to present these results to the experts, who will review them for clinical validity. A successful adaptive planning workflow will create plans that experts consider more clinically beneficial than the original plans.

'''Aim 2. '''We will work closely with Core 1 to explore and optimize deformable registration and automatic segmentation algorithms available in NA-MIC Kit. The most promising algorithms for registration include demon’s, B-spline, and thin-plate splines. For segmentation good candidates include graph-cut and level set methods. Because expert segmentation is available from the original scan, we will design new algorithms that use expert segmentation to guide the segmentation of subsequent scans. Registration and segmentation accuracy will be evaluated by comparison with expert markup. The use of non-rigid registration algorithms for automatic segmentation of images in adaptive radiotherapy, and for mapping of the prior radiation plans onto an updated scan, represent a significant innovation in the fi eld of radiation therapy.

'''Aim 3. '''To evaluate adaptive radiotherapy against traditional radiotherapy, we will apply dose response models to hypothetical treatments using both strategies. Plans will be generated based on the initial CT, and simulated on subsequent CT scans. Dose to the clinical target volume (CTV) and organs at risk will be analyzed using site-specific metrics, including minimum dose, maximum dose, and dose volume histograms. The focus of the study will be on proton-beam treatments, but also we will evaluate intensity-modulated radiation therapy (IMRT) for photon treatments. Dosimetry results from this study will be used to predict tumor control
probability (TCP) and normal tissue complication probability (NTCP) using logistic and Lyman models [5-7].

Analysis of TCP and NTCP will be used to estimate the percentage of patients who will benefit from the adaptive treatment strategy. The PI, Gregory Sharp, PhD, is a clinical medical physicist with research interests in image-guided radiotherapy. Annie Chan, MD, will lead the clinical evaluation. Dr. Chan is a radiation oncologist who specializes in head and neck cancer, with research interests in proton-beam therapy.

==Connections between this work and any of the other 3 proposed DBPs==
This DBP shares the basic needs of the other DBPs for new image registration in the presence of
pathology, advanced segmentation tools for subject-specific analysis, shape analysis and deformable shape segmentation tools, and longitudinal analysis for effi cient processing of baseline and follow-up scans. The development of new tools and workflows will involve close interaction with Core 1 algorithms and engineering.

==Deliverables, Timeline, Impact==
The scientific deliverables of this DBP are: (1) to demonstrate the use of an open-source software platform for adaptive radiotherapy, and (2) to evaluate the therapeutic gain of adaptive radiotherapy in protonbeam treatments of the paranasal sinus and nasopharynx. In addition to presenting and publishing our scientific results, we will host two educational workshops dedicated to the use of NA-MIC tools for adaptive radiotherapy.

In year 2, we will host a special 1-day workshop on the MGH campus. During year 3, we will host a tutorial session at a major national or international radiotherapy conference, such as the AAPM Annual Conference or the Annual ASTRO Meeting. MGH investigators will interact directly with NA-MIC Computer Science Core PIs in developing new and refining
existing algorithms for addressing the issues inherent to adaptive radiotherapy. DBP activities at MGH will occur in consultation with the Primary Technical NA-MIC DBP Contact Algorithm (Golland) and Engineering (Pieper) contacts. Once completed and rigorously validated, these workflows will (1) be applied to the patient data described above, (2) made available for open dissemination via the NA-MIC website, and (3) form the basis for training and educational materials for NA-MIC investigators and the adaptive radiotherapy community.
Results will be featured in presentations at scientific conferences, organized training events/workshops, etc., as a way to disseminate tool capabilities and, where possible, tutorials on how to use the NA-MIC technology for other projects related to adaptive radiotherapy (see also Training and Dissemination Cores). Finally, the DBP PI will attend each NA-MIC All-Hands-Meeting to discuss the DBP with NA-MIC PIs, report on developments,
and progress. NA-MIC will benefit from this DBP by exposure to the field of radiotherapy and, in particular, to therapy planning, a clinical area which is known to critically rely on efficient and robust 3D image registration and segmentation.

The specific aims of this project are appropriate in scope for a three-year DBP project. However, we see an opportunity for collaboration on related topics, including adaptive therapy for lung cancer, 4D treatment planning, functional imaging for radiotherapy planning, and workflow management. Following the DBP, we will pursue one or more of these ideas as a collaboration grant with NA-MIC.

==References==
#Seer 2007. Cancer Survival Among Adults: U.S. SEER Program, 1988-2001, Patient and Tumor Characteristics (Bethesda, MD: National Cancer Institute, SEER Program, 2007) 2007.
#Hansen EK, Bucci MK, Quivey JM, Weinberg V, Xia P. Repeat CT imaging and replanning during the course of IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2006;64(2):355-62. PMID: 16256277.
#Wu Q, Chi Y, Chen PY, Krauss DJ, Yan D, Martinez A. Adaptive replanning strategies accounting for shrinkage in head and neck IMRT. Int J Radiat Oncol Biol Phys. 2009;75(3):924-32. PMID: 19801104.
#Salama JK, Haddad RI, Kies MS, Busse PM, Dong L, Brizel DM, et al. Clinical practice guidance for radiotherapy planning after induction chemotherapy in locoregionally advanced head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2009;75(3):725-33. PMID: 19362781.
#Burman C, Kutcher GJ, Emami B, Goitein M. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys. 1991; 21:129-35. PMCID: PMID: 2032883.
#Okunieff P, Morgan D, Niemierko A, Suit HD. Radiation dose-response of human tumors. Int J Radiat Oncol Biol Phys. 1995;32(4):1227-37. PMID: 7607946.
#Eisbruch A, Ten Haken RK, Kim HM, Marsh LH, Ship JA. Dose, volume, and function relationships in parotid salivary glands following conformal and intensity-modulated irradiation of head and neck cancer. Int J Radiat Oncol Biol Phys. 1999;45(3):577-87.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

DBP:Prostate Cancer

2016-02-09T04:32:56Z

Zack: Update from Wiki

=Prostate Cancer Solutions=
{|class="wikitable"
|[[Image:FIG.2-10.TRProstateBiopsy2-c.png|400px]]
|
*'''Data''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Prostate: 5 robot-assisted intervention cases for Prostate Cancer]
*'''Tutorial''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials Transrectal MR Guided Prostate Biopsy and Perkstation Slicer Tutorial]
*'''Software for Slicer 3.6''' [http://www.slicer.org/slicerWiki/index.php/Modules:ProstateNav-Documentation-3.6 ProstateNav]
*'''Representative Publications''' [http://www.na-mic.org/publications/item/view/1659 1659] | [http://www.na-mic.org/publications/item/view/1810 1810]
*'''Final Report''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:QueensFinal:2010 Queen's University]
|}

===Segmentation and Registration Tools for Robotic Prostate Interventions===

Dr. Fichtinger and colleagues from Queens University are investigating methods to improve prostate cancer diagnosis and therapy through the use of machine robotics. Before collaborating with NA-MIC, Dr. Fichtinger had been developing a custom software application to integrate various segmentation, registration, organ tracking, and robotic control algorithms into a clinically usable system for treating prostate cancer. The development team had encountered several systemic limitations. First, significant effort was required to implement basic functionalities, such as image visualization, graphical user interface (GUI), and data management. Second, with each new integration of algorithms and features, the overall complexity of the software kept increasing, making it difficult for users to learn and for developers to maintain and enhance the system. Additionally, it had proved difficult to find collaborators with the appropriate technical capability and the willingness to learn a customized software and assist them to develop new methods within this framework. Through its collaboration with NA-MIC, this DBP has been able to build an application for their cutting-edge robotics technology. By leveraging existing NA-MIC tools, they have avoided duplicative work to implement basic functionalities and have developed a wide range of segmentation and registration algorithms. Moreover, as a consequence of the modular architecture of 3D Slicer, they have been able to integrate all of their previous algorithms into one framework and combine them with functionalities already implemented by others. As a result, this DBP now has a single software with manageable complexity that works with multiple generations of the prostate robot. As a consequence of this successful collaboration, Dr. Fichtinger and colleagues have started to use 3D Slicer as a basis for several new computer-aided intervention software applications, including an augmented reality image overlay system for needle insertion (Perk Station), a test bench for real-time monitoring of tissue ablation, and a gynecologic radiotherapy system. The open source robot control and treatment planning platform developed under NA-MIC has empowered this DBP to participate in several major international and national alliances in the USA, Canada, and Austria.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

DBP:TBI

2016-02-09T04:32:55Z

Zack: Update from Wiki

__NOTOC__
=TRAUMATIC BRAIN INJURY=
'''PI: Jack Van Horn, UCLA'''

In vivo neuroimaging is an increasingly relevant means for the neurological assessment of traumatic brain injury (TBI) [1]. However, standard automated image analysis methods are not sufficiently robust with respect to TBI-related changes in image contrast, brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury. Associating multimodal quantification of brain insults with clinical and outcome metrics is a particular challenge. This DBP employs a multi-modal approach coupled with clinical outcome measures to build computational models for guided and semi-automatic TBI analysis and quantification with a view toward assessing clinical improvement under the following Specific Aims.

==Specific Aims==

[[Image:DBP.TBI1.png|500px|left|thumb|Figure 1. '''A''' TBI injuries present on T1-weighted anatomical images appear as hyperintensities in brain tissue. The region of the frontal pole and middle temporal gyrus show evidence of TBI damage (yellow arrows). '''B''' TBI data, a medical image processing challenge: Voxel intensity processing for tissue classification proximal to the site of cranial fracture (blue arrow) may require special identification for algorithms to conditionalize processing. Likewise, where surgical intervention has occurred (yellow arrow), bone and scalp may not form coherent boundaries which may cause segmentation algorithms to mis-assign voxels. Finally, MR compatible electrodes of surgical anchors may exist in the image volume (green arrow) and may cause various algorithms to confuse these with part of the skull/scalp, potentially resulting in mis-registration, mis-segmentation, or warping to anatomical templates.]]

'''1.''' Develop end-to-end processing approaches using the NA-MIC Kit to investigate alterations in cortical thickness, and subsequent ventricular and white matter changes in patients with TBI and in age-matched controls. Image processing will include segmentation of lesions, hemorrhage, edema, and other pathology relevant to TBI. Longitudinal changes will be assessed by registration and joint segmentation of baseline and follow-up data.

'''2.''' Develop robust workflows for diffusion weighted imaging (e.g. DTI, HARDI) datasets from TBI patients, by using the NA-MIC Kit and Slicer to obtain reliable and robust metrics of white matter pathology and of white matter changes due to therapy and/or recovery.

'''3.''' Using the NA-MIC Kit, cross-correlate multimodal metrics of cortical thickness, complexity, ventricular volume, and lesions from structural imaging and white matter fiber integrity from diffusion tensor imaging, with clinical outcome variables, i.e., time since injury, age, gender and other potential factors predictive of recovery (Figure 1). Emphasis is placed on the feasibility of subject-specific analysis, as opposed to population-based averaging, to examine the influence of TBI on time-dependent alteration of gray and white matter integrity with accompanying change in clinical outcome variables to be used in subsequent TBI assessment.

==Background==
Traumatic Brain Injury, also called diffuse axonal injury (DAI), acquired brain injury or, simply, head injury, occurs when a sudden trauma causes damage to the brain [2]. TBI results when the head suddenly and violently hits an object, or when an object pierces the skull and enters brain tissue. Symptoms can be mild, moderate, or severe, depending on the extent of the damage to the brain [3]. Common disabilities include deficits in cognition, sensory processing, communication, and behavior or mental health [4-6]. Severe TBI may result in ''stupor,'' individual can be aroused briefly by a strong stimulus (e.g., sharp pain); ''coma,'' individual is totally unconscious, unresponsive, unaware, and unarousable; ''vegetative state,'' individual is unconscious and unaware of his or her surroundings, but continues to have a sleep-wake cycle and periods of alertness; and ''persistent vegetative state,'' individual remains unresponsive for more than a month [7].

According to the Centers for Disease Control and Prevention [8], TBI affects 1.4 million Americans annually resulting in $60 billion in healthcare costs (in year 2000). Of those affected yearly, 50,000 die, 235,000 are hospitalized for some length of time, and 1.1 million are treated and released from a hospital emergency department. The CDC also estimates that
at least 5.3 million Americans currently have a long-term or lifelong need for help to perform activities of daily living as a result of TBI. Few, if any, follow-up treatment options exist. Accordingly, ~40% of those hospitalized with TBI have at least one unmet need for services 1-year post-injury. These unmet needs include improving memory and problem-solving; managing stress and emotional upsets; and improving one’s job skills. The most common demographic for TBI is 16-24 y.o. males, who are affected at a critical time of learning and formal entry into the U.S. workforce [7]. Principal among this demographic are returning Iraq and Afghanistan war veterans [9]. The long-term effects of TBI include epilepsy and increased risk for diseases such as Alzheimer’s [10], Parkinson’s [11], and other brain disorders that become more prevalent with age [12]. The magnitude of this medical concern to the U.S. cannot be over-stated.

'''Neurological Concomitants of TBI.''' Following TBI, a cascade of neuroanatomical alterations initiate, with diffuse alterations in cortical structure peripheral to the point of injury but also distributed throughout the brain [6]. Notably, there is ventricular enlargement and cortical thickness changes remote from the site of the TBI. White matter connectivity can be significantly altered with greatly reduced efficiency of signal transduction over affected pathways or complete cessation of inter-regional communication due to axonal damage. These anatomical derangements can have profound effects on speech, motor, and cognitive processes [5, 13]. The extent of change is putatively related to the severity of TBI, location, subject age, and post-injury reatment, among other factors.

[[Image:DBP.TBI2.png|500px|left|thumb|Figure 2. ''Left.'' Guided T1 image segmentation using Slicer. Cross hairs centered on areas of right frontal brain damange. See also region of contra-coup in occipital cortex (yellow). ''Right.'' Peribleed tractography with fiducials for the subclusters/tracts, showing fiber clustering in relation to TBI lesion. Lesion loci and white matter damage can suggest specific neuropsychological assessments.]] '''Computational and Data Processing Issues.''' On anatomical MRI scans, TBI-related insults can appear as hyper-intensities (Figure 1A), which vary in magnitude and extent, the degree to which tends to correlate with clinical symptoms [14]. Additionally, in severe TBI, sections of skull that are fractured during the injury or removed during surgical intervention (Figure 1B) may not form a contiguous boundary to enable efficient digital removal of bone and other non-brain tissues. This, in turn, complicates tissue segmentation, regional parcellation, the measurement of ventricular size, cortical thickness, and other metrics [15]. Computational algorithms require refinement to include constraints to account for TBI-related signal alterations in anatomical scans; e.g., users may have to manually indicate regions encompassing the site of injury on the scans to guide local processing around the site and to reduce the weight of these regions on other, nonaffected brain areas. Alternatively, probabilistic classifiers may need to include an extra classification for voxels with tissue properties that have been altered by TBI (Figure 2). This is particularly the case in diffusion weighted imaging (DWI) where the presence of TBI-related alterations in signal may reflect specific damage to white matter proximal to the lesion as well as long reaching effects along tracts to peripherally connected regions of cortex [3,16,17].

'''Use of NA-MIC Resources and the NA-MIC Kit.''' Using sophisticated NA-MIC tools, this DBP is developing end-to-end processing solutions by which to examine TBI neuroimaging data. The NA-MIC Kit encompasses a collection of tools for automated or semi-automated processing of medical imaging data. Notable among these is the [http://www.itk.org Insight Toolkit] [18] for use in brain registration and segmentation via the [http://www.slicer.org 3D Slicer] software platform [19]. These software tools may be linked to form data processing workflows that can process data via end-to-end solutions that may be shared with others, posted on websites, and used in training materials. They form an excellent platform for user-guided, patient-specific analysis, but require additional development to inform the program about regions where TBI-related signal changes may necessitate alteration of model parameters or search volumes.

==Investigators==
{|class="wikitable"
|-
|'''NAME'''
|'''DEGREE'''
|'''INSTITUTION'''
|'''EXPERIENCE'''
|'''ROLE'''
|-
|[http://www.loni.ucla.edu/About_Loni/people/Indiv_Detail.jsp?people_id=220 JACK VAN HORN]
|PH.D.
|UCLA
|NEUROSCIENCE & ENGINEERING
|DBP PI
|-
|[http://neurosurgery.ucla.edu/body.cfm?id=657 DAVID HOVDA]
|PH.D.
|UCLA
|NEUROSURGICAL OUTCOMES
|INVESTIGATOR
|-
|[http://www.uclahealth.org/body.cfm?id=458&action=detail&ref=14076 PAUL VESPA]
|M.D.
|UCLA
|NEUROINTENSIVIST
|INVESTIGATOR
|-
|[http://www.loni.ucla.edu/About_Loni/people/indiv_detail.php?people_id=1 ARTHUR TOGA]
|PH.D.
|UCLA
|NEUROSCIENCE & INFORMATICS
|INVESTIGATOR
|-
|[http://faculty.bri.ucla.edu/institution/personnel?personnel_id=45540 JEFFREY ALGER]
|PH.D.
|UCLA
|NEUROIMAGING
|INVESTIGATOR
|-
|[http://www.sci.utah.edu/people/gerig.html GUIDO GERIG]
|PH.D.
|UTAH
|COMPUTER SCIENCE
|LEAD TEDHNICAL CONTACT
|-
|[http://www.kitware.com/company/team/aylward.html STEPHEN AYLWARD]
|PH.D.
|UNC
|ENGINEERING
|LEAD TECHNICAL CONTACT
|}

==Methods==

'''Analysis Software Tools and Data Processing Protocol.''' Specifically adopt the NA-MIC Kit open source software platform consisting of Slicer, tools and toolkits such as VTK and ITK, and software engineering methodologies for multiplatform implementation. Using ITK, data will be intensity normalized and bias-field corrected; tissue types will be segmented interactively to assist probabilistic classification; cortical thickness will be determined along the entire cortical sheet as the linear distance between the outer edge of the cortical surface and the grey-white matter boundary. Ventricular size will be determined by a space filling algorithm, while shape will be characterized using LONI tools for shape decomposition and quantitative description. DWI processing routines will be developed to better account for TBI-related changes in diffusion metrics. Results from multimodal analyses will be visualized using VTK, Slicer, and other suitable platforms.

'''Exemplar Anatomical Data.''' Examine neuroimaging data obtained from TBI patients to rigorously assess workflows using the NA-MIC Kit. MRI volumes from 202 subjects will be drawn from the LONI Image Data Archive (IDA), a comprehensive neuroimaging data archive comprised of a number of funded projects. Samples will include patients who have suffered from TBI (N=160; 22F, 138M) and age-matched normal controls (N=42; 13F, 29M). Mean ages for males is 33.8±9.2 and for females is 33.6±9.9. T1-weighted whole brain MPRAGE volumes, T2, and, in subjects with available data sets, diffusion weighted imaging (DTI/HARDI) collected at 1.5 and 3.0T will be utilized. Additional data include a variety of MR imaging modalities and NAMIC
workflows will be crafted to accommodate them.

'''Data Analysis and Expected Results.''' Obtain multimodal results using Slicer software tools, specifically developed under NA-MIC using ITK, VTK, for the analysis of neurological concomitants of TBI. Metrics will be extracted and imported into purpose-built software for univariate and multivariate modeling to provide additional insights to that of previous work on the role of neuroanatomical changes occurring in TBI on outcome variables predicting degree of change and/or recovery. Several primary hypotheses using individual
and repeated imaging include: (1) Cerebral atrophy (regional and global) occurs at a faster rate in diffuse vs. focal TBI; (2) Rates are dependent upon initial injury severity; (3) Ongoing or progressive change continues up to 1 year post-TBI; and (4) Secondary insults increase the rate and extent of the initial TBI. We will also examine age-at-lesion effects, since these factors are likely to impact on measures of the degree of loss of developmental
and life-span neuroplasticity believed to follow TBI. Using DWI data, we will assess the effects of TBI on mean diffusivity, fractional anisotropy, and their potential as clinical outcome correlates. Complete multimodal data processing solutions using the NA-MIC Kit and associated tools will be made openly available, with accompanying training materials via the NA-MIC web site, and comply with the NA-MIC open-source policies.

'''Clinical Utility.''' The NA-MIC Kit workflows developed under this program are intended for application to TBI clinical practice and patient monitoring. We do not envision the tools being used to pool brain image data across subjects (e.g. the creation of brain atlases, per se) but for use in assessing extent of traumatic brain damage and measuring change over time in individual subjects. However, with knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and used to suggest viable treatment options for a subject against patterns typical of TBI patients.

==Connections between this work and any of the other 3 proposed DBPs==
Considerable interactions presently exist between the NA-MIC project and the Laboratory of Neuro Imaging (LONI), and have resulted in several collaborative peer-reviewed publications, underscoring the suitability of this collaboration. Work under this DBP involves direct interaction with the Engineering team, as well as the Head and Neck Cancer DBP project being conducted at MGH.

==Deliverables, Timeline, Impact==
{|class="wikitable"
|-
|'''SPECIFIC AIMS'''
|'''Year 1'''
|'''Year 2'''
|'''Year 3'''
|-
|'''Aim 1'''
|Design and evaluate end-to-end processing approaches for segmentation of multimodal images Cortical thickness, segmentation of ventricles, lesions, bleedings, hemorrhage
|Evaluate registration of initial and followup scans, extend segmentation processing to joint segmentation of longitudinal image data, development of workflows, documentation
|Develop framework for assessment of longitudinal changes of altered brain anatomy and pathology w.r.t.. brain plasticity and secondary neuroanatomical effects
|-
|'''Aim 2'''
|Test existing DWI processing modules, design of refined tools for analysis of white matter pathology in TBI
|Develop workflows for efficient extraction and analysis of DWI data from TBI patients, extension to serial data of individual patients
|Use workflows and 3D Slicer visualizations to assess white matter changes due to therapy and/or recovery, documentation
|-
|'''Aim 3'''
|Develop schemes descriptive for TBI for specification of metrics from segmentations of gray and white matter brain alterations and pathology
|Establishing image-derived multimodal metrics of gray and white matter pathology in TBI, extends metrics by including longitudinal changes
|Cross correlation of multimodal metrics with clinical outcome variables to evaluate potential factors that are predictive of recovery and can inform and guide clinical assessment
|}

==References==

#Furlow B. Diagnostic imaging of traumatic brain injury. Radiol Technol. 2006;78(2):145-56; quiz 57-9.
#Narayan RK, Michel ME, Ansell B, Baethmann A, Biegon A, Bracken MB, et al. Clinical trials in head injury. J Neurotrauma. 2002;19(5):503-57. PMCID: 1462953.
#Miles L, Grossman RI, Johnson G, Babb JS, Diller L, Inglese M. Short-term DTI predictors of cognitive dysfunction in mild traumatic brain injury. Brain Inj. 2008;22(2):115-22. PMID: 18240040.
#Fleminger S. Long-term psychiatric disorders after traumatic brain injury. Eur J Anaesthesiol Suppl. 2008;42:123-30. PMID: 18289429.
#McDonald BC, Flashman LA, Saykin AJ. Executive dysfunction following traumatic brain injury: neural substrates and treatment strategies. NeuroRehabilitation. 2002;17(4):334.
#Satz P, Zaucha K, Forney DL, McCleary C, Asarnow RF, Light R, et al. Neuropsychological, psychosocial and vocational correlates of the Glasgow Outcome Scale at 6 months post-injury: a study of moderate to severe traumatic brain injury patients. Brain Inj. 1998;12(7):555-67. PMID: 9653519.
#Maas AI, Stocchetti N, Bullock R. Moderate and severe traumatic brain injury in adults. Lancet Neurol. 2008;7(8):728-41. PMID: 18635021.
#CDC (http://www.cdc.gov/NCIPC/tbi/Fact-Sheets/Facts_About_TBI.pdf).
#Connors S, Gordon WA, Hovda DA. Care of war veterans with mild traumatic brain injury. N Engl J Med. 2009;361(5):536-7; author reply 7-8. PMID: 19645084.
#Van Den Heuvel C, Thornton E, Vink R. Traumatic brain injury and Alzheimer’s disease: a review. Prog Brain Res. 2007;161:303-16. PMID: 17618986.
#Hirtz D, Thurman DJ, Gwinn-Hardy K, Mohamed M, Chaudhuri AR, Zalutsky R. How common are the “common” neurologic disorders? Neurology. 2007;68(5):326-37. PMID: 176261678.
#Tupler LA, Krishnan KR, McDonald WM, Dombeck CB, D’Souza S, Steffens DC. Anatomic location and laterality of MRI signal hyperintensities in late-life depression. J Psychosom Res. 2002;53(2):665-76. PMID: 12169341.
#Ruttan L, Martin K, Liu A, Colella B, Green RE. Long-term cognitive outcome in moderate to severe traumatic brain injury: a meta-analysis examining timed and untimed tests at 1 and 4.5 or more years after injury. Arch Phys Med Rehabil. 2008;89(12 Suppl):S69-76.
#Dubroff JG, Newberg A. Neuroimaging of traumatic brain injury. Semin Neurol. 2008;28(4):548-57. PMID: 18843581
#Poca MA, Sahuquillo J, Mataro M, Benejam B, Arikan F, Baguena M. Ventricular enlargement after moderate or severe head injury: a frequent and neglected problem. J Neurotrauma. 2005;22(11):1303-10. PMID: 16305318.
#Kumar R, Husain M, Gupta RK, Hasan KM, Haris M, Agarwal AK, et al. Serial changes in the white matter diffusion tensor imaging metrics in moderate traumatic brain injury and correlation with neurocognitive function. J Neurotrauma. 2009;26(4):481-95. PMID: 19196176.
#Niogi SN, Mukherjee P, Ghajar J, Johnson C, Kolster RA, Sarkar R, et al. Extent of microstructural white matter injury in postconcussive syndrome correlates with impaired cognitive reaction time: a 3T diffusion tensor imaging study of mild traumatic brain injury. AJNR Am J Neuroradiol. 2008;29(5):967-73. PMID: 18272556.
#Pieper S., Lorensen B., Schroeder W., Kikinis R. [http://www.na-mic.org/publications/item/view/68 The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as an Open Platform for the Medical Image Computing Community.] Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006; 1:698-701.
#Pieper S., Halle M., Kikinis R. [http://www.na-mic.org/publications/item/view/91 3D SLICER.] Proceedings of the 1st IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2004; 1:632-635.
#Van Horn JD, Toga AW. Multisite neuroimaging trials. Curr Opin Neurol. 2009;22(4):370-8. PMCID: 19506479.
#Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain. 007;130(Pt10):2508-19. PMID: 17872928.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

Driving Biological Projects

2016-02-09T04:32:54Z

Zack: Update from Wiki

__NOTOC__
=Driving Biological Projects=

{|border=0 align=left
|style="width:175px"|[[image:Big-DBP-Logo.png|100px|link=DBP:Overview|<big>Overview</big>]]
|-
|[[DBP:Overview|Overview]]
|}

 
== ==
To ensure that, at the end of the day, demonstrable healthcare improvements are achieved, Driving Biological Projects (DBPs) are selected to guide research development. The role of a NA-MIC DBP is to:

*define a clinical problem
*provide a clinical dataset (individual or population)
*collaborate with algorithms scientists to develop a solution
*work with software engineers to create end-to-end applications for clinical users

{|class="wikitable"
|style="width:175px"|[[image:DBP.AF1-c.png|120px|link=DBP:Atrial_Fibrillation]]
|style="width:175px"|[[image:DBP.HD1-c.png|120px|link=DBP:HD]]
|style="width:175px"|[[image:DBP.HNC1-c.png|100px|link=DBP:Head and Neck Cancer]]
|style="width:175px"|[[image:DBP.TBI-c.png|100px|link=DBP:TBI]]
|-
|style="width:175px"|[[DBP:Atrial_Fibrillation| Atrial Fibrillation]]
|style="width:175px"|[[DBP:HD|Huntington's Disease]]
|style="width:175px"|[[DBP:Head and Neck Cancer|Head and Neck Cancer ]]
|style="width:175px"|[[DBP:TBI| Traumatic Brain Injury]]
|-
|colspan=4|At the inception of NA-MIC, the focus of biological project development was centered on schizophrenia. Schizophrenia provided a rich resource of neuroimaging data and a pressing need for new imaging technologies to unlock the white matter regions of the brain. The DBPs contributing to this effort were based at Harvard Medical School, University of California at Irvine, Dartmouth College, Indiana University, and University of Toronto. In the ensuing years, the scope of project development expanded to include a broader range of diseases. Links are provided to disease-specific datasets, tutorials, software, representative peer review publications, and notes maintained by the individual DBPs on NA-MIC's interactive Wiki.
|-
|style="width:150px"|[[image:FIG.2-8.IOFF-excludearcuate-cropped-c.png|150px|link=http://www.na-mic.org/pages/DBP:Schizophrenia]]
|style="width:150px"|[[image:BrainVentriclesAndLesions-c.png|150px|link=http://www.na-mic.org/pages/DBP:Lupus]]
|style="width:150px"|[[image:FIG.2-10.TRProstateBiopsy2-c.png|150px|link=http://www.na-mic.org/pages/DBP:Prostate_Cancer]]
|style="width:150px"|[[image:CorticalThickness_GreyMatter-c.png|150px|link=http://www.na-mic.org/pages/DBP:Autism]]
|-
|style="width:150px"|[[DBP:Schizophrenia| Schizophrenia]]
|style="width:150px"|[[DBP:Lupus| Lupus]]
|style="width:150px"|[[DBP:Prostate_Cancer| Prostate Cancer]]
|style="width:150px"|[[DBP:Autism|Autism]]
|}

ImageArchive

2016-02-09T04:32:53Z

Zack: Update from Wiki

__NOTOC__
=Featured Image Archive=

*[[ImageArchive/2009| 2009]]
*[[ImageArchive/2008| 2008]]
*[[ImageArchive/2007| 2007]]

==March 2010==
[[Image:Paniagua-InsightJournal2009-fig11.jpg.png|left|485px|thumb|'''Featured Image: Local Shape Analysis using MANCOVA.''' Morphological correlation with pain variables (pain onset). Top: Pearson correlation coefficients. Bottom: Associated statistical significance maps. The significance maps show statistically significant correlations in the the lateral and posterior surfaces of the condyles. [http://www.na-mic.org/publications/item/view/1704 Read more...]]]

DBP:Atrial Fibrillation

2016-02-09T04:32:34Z

Zack: Update from Wiki

__NOTOC__
=ATRIAL FIBRILLATION=
'''PI: Rob MacLeod, University of Utah'''

[[Image:DBP.AF1.png|400px|left|thumb|Figure 1: '''A''' The platinum-tipped catheter connected to the radiofrequency (RF) energy generator is advanced into the left atrium (LA) to the wall of the chamber, where bursts of RF energy ablate small regions of tissue. '''B''' CT of LA and pulmonary veins (PV). '''C''' Slicer-created image showing posterior view of segmentation of the LA, aorta, and PV (red) and superimposed segmentation of regions of late godolinium enhancement (LGE)(green) from post-ablation MRI.]]

Approximately 0.5% of patients have AF in the 50 to 59 year age group, and up to 9% have AF in the 80 to 89 year age group. Moreover, the prevalence is increasing [1]. AF is associated with increased morbidity (i.e., stroke) and mortality. AF also poses a significant burden on healthcare and is associated with an annual estimated cost of 7 billion US dollars [2]. Yet, despite its high incidence and financial impact, AF management remains unsatisfactory. Traditional treatments to restore and maintain normal heart rate, namely, electrical cardioversion followed by initiation and lifelong maintenance with antiarrhythmic drugs [3], fail in most patients [4-6]. Catheter ablation is a rapidly emerging alternative. This curative approach seeks to suppress the sources of electrical dysynchrony by converting the cells responsible for the arrhythmia to inactive scar tissue. Radiofrequency (RF) energy is applied through a specialized catheter introduced via the venous system into the left atrium (LA) of the heart (Figure 1). This approach offers a true cure, obviating the need for lifelong medication. The success rate, however, is low at only 40-80%. At least two major obstacles to this treatment approach have been identified: (1) inability to distinguish which patients will (or will not) benefit from the procedure, and (2) inability to rapidly identify the extent of lesion ablation immediately post-procedure, as well as subsequent to scarification. In this regard, MRI offers the most promising technology to overcome these obstacles. Rapid, automated image processing and analysis have been identified as the rate-limiting steps to the development of practical MRI-based therapies.

==Specific Aims==

1. '''Develop and validate image-based longitudinal diagnostic indices for AF.''' We will (1) apply emerging registration technologies that use image and shape-based priors to study longitudinal changes in atrial morphology; (2) develop nonlinear registration approaches that do not require identification of common landmarks between image sets or between different imaging modalities; and (3) implement new methods to achieve robust multimodal and pre-/post-imaging registration in the face of uncompensated image distortion from respiration and cardiac motion and in the presence of local intensity changes due to treatment.

2. '''Develop automatic segmentation methods for the atrium and adjacent structures.''' Segmentation is the essential step for image-based analysis of patients with AF because it provides a quantitative index of the abnormal tissue substrate. Current segmentation software is largely manual and slow, limiting its utility to guide the ablation procedure. It also is technically simplistic, as it is based on setting intensity thresholds to differentiate normal from abnormal regions. To achieve this aim, we will integrate and implement more efficient and sophisticated 3D approaches into our workflow; help develop suitable priors; and establish effective parameters. These methods then will be validated against manual segmentation by experts. We anticipate that pre-ablation assessment of fibrosis, peri-ablation evaluation of lesions, and post-ablation visualization of scar formation each will require a different approach. Success in this aim will improve the efficiency of our own studies and provide tools for dissemination to our partner labs, thus enabling the first multicenter trial of MRI for AF management.

3. '''Develop an AF scoring scheme to evaluate disease progression and recovery from therapy.''' The NA-MIC Kit and Slicer 3 will be used to integrate, test, and provide feedback for all of the components of a fully integrated software environment that can provide segmentation, registration, and quantitative shape analysis capabilities for MRI-based management of AF. This workflow will incorporate other non-image information necessary to establish the scoring scheme. Such schemes exist for other forms of heart disease and are invaluable in setting standards for care, determining the appropriate time for intervention, and for screening and preventative medicine. The availability of a non-invasive scheme for monitoring disease progression will revolutionize the management of AF. To implement this scheme, we will require efficient algorithms, developed within the technical cores of NA-MIC, and an intuitive customizable user interface that gives open access to a database of previous results and population information. The system described would be new to our field and would fill a critical need.

==Background==
Tissue remodeling of the atrial wall is the hallmark of AF, a progressive disease that develops over time (months to years). Although AF may have a specific genetic component [7, 8], it is not a typical congenital disease. All hearts are capable of sustaining chronic AF in the presence of rapid stimulation. Hence, the expression “Atrial fibrillation begets atrial fibrillation” [9]. The mechanisms for this transformation are only partially known [9, 10]. However, the current focus on tissue remodeling and its putative role in AF makes the image-based approach ideal.

The Comprehensive Arrhythmia Research and MAnagement (CARMA) Center at the University of Utah is a world leader in the rapidly emerging field of MRI-managed evaluation and ablation of AF [11-15]. Other groups have begun to recognize the potential of this approach [16-18] and to investigate and validate some of our findings [19, 20]. Still others are developing new refinements of the MRI technique driven by the specific needs of this application domain [21-25].

==Investigators==

{|class="wikitable"
|-
|'''NAME'''
|'''DEGREE'''
|'''INSTITUTION'''
|'''EXPERIENCE'''
|'''ROLE'''
|-
|[http://www.sci.utah.edu/people/macleod.html ROB MACLEOD]
|PH.D.
|UTAH
|BIOENGINEERING
|DBP PI
|-
|[http://www.sci.utah.edu/people/cates.html JOSH CATES]
|PH.D.
|UTAH
|COMPUTER SCIENCE
|RESEARCH ASSOCIATE
|-
|[http://www.bme.gatech.edu/groups/bil/ ALLEN TANNENBAUM]
|PH.D.
|GEORGIA TECH
|COMPUTER SCIENCE
|LEAD TECHNICAL CONTACT
|-
|[http://www.cs.utah.edu/~whitaker ROSS WHITAKER]
|PH.D.
|UTAH
|COMPUTER SCIENCE
|LEAD TECHNICAL CONTACT
|-
|[http://www.na-mic.org/Wiki/index.php/User:Millerjv JIM MILLER]
|PH.D.
|GE GLOBAL
|RESEARCH ENGINEERING
|LEAD TECHNICAL CONTACT
|}

==Methods==
Preliminary investigation by CARMA has identified image processing and analysis as the rate-limiting step to the development of MRI-based therapies. Novel forms of MRI can be used to evaluate new patients, predict success before ablation [12-14], analyze outcomes post-ablation, and guide repeat ablations [11]. While the results have been promising [16-18], MRI-based therapies urgently require advanced tools and software to support efficient workflows and accelerate the quantification and analysis of images. Processing each MR image set from a new patient requires extensive segmentation of organs and regions within organs.

This process currently takes 2-3 hours of manual labor per case by experienced technicians. Moreover, the results, while quantitative, display large variance because of the subjective nature of critical thresholds. To effectively deliver catheter ablation, the post-ablation analysis (i.e., rapid registration of pre- and post-ablation images, segmentation of the atria, identification of tissue changes from ablation, and interactive visualization of
the heart and ablated regions) must be accomplished within 30 minutes, while the patient is still on the procedure table and available for adjustments. Current practice, even in advanced labs, is constrained by the labor-intensive image analysis process. The lack of adequate image processing techniques hinders the interpretation of post-ablation imaging, often necessitating a full repeat procedure for recurrent AF at a later date, which increases costs and patient discomfort. An integrated customized set of automated software tools for image processing and analysis, based on state-of-art 3D image processing, would dramatically improve all stages of patient management. The NA-MIC Kit, Slicer, and supporting algorithms form the basis for such development.

[[Image:DBP.AF2.png|300px|left|thumb|Figure 2: Survival curve showing the rate of continued normal sinus rhythm as a function of time after ablation for patients grouped according to the level of late gadolinium enhancement (LGE) on MRI images acquired before ablation therapy.]]
A survival curve showing the rate of continued normal sinus rhythm as a function of time after ablation for patients grouped according to the level of late gadolinium enhancement (LGE) on MRI images acquired before ablation therapy is depicted in Figure 2. This curve shows promising findings from CARMA patients and illustrates the need for improvements in image analysis. These results suggest that late gadolinium enhancement (LGE), which is thought to be a measure of tissue fibrosis, is predictive of outcome in ablation. Patients with very low levels of LGE enhancement (< 5%) have 100% maintenance of normal rhythm while those with extensive enhancement (> 35%) have only about 10% success after one year. Although these extremes are useful for differentiating ideal patients from those with little chance of success, these two groups account for less than 10% of AF patients. We need to define enhancement thresholds that can predict risk for intermediate levels of enhancement. Current methods are not sufficiently accurate or robust for performing segmentations or identifying enhancement.

Improved algorithms for image processing and analysis are needed before radiofrequency ablation can be evaluated in all patients irrespective of the level of their disease.
Achieving these specific aims will require close, bidirectional interactions with the Algorithms team, a task greatly facilitated by our close physical proximity at Utah and by the choice of a highly experienced postdoctoral fellow to carry out the technical steps. Members of the Algorithms team already have received briefings on existing practices, explanations of the nature of the clinical needs, and have access to many examples of clinical MRI data from over 800 separate scans currently in the CARMA database. These interactions have produced
promising preliminary findings, including landmark-free registration of atrial geometry and statistical shape analysis of left atrial morphology across patient groups and longitudinally for individual patients as they recover after ablation therapy. An example of some of our preliminary results is shown in Figure 3.
 

[[Image:DBP.AF3.png|600px|left|thumb|Figure 3. Particle system correspondence on the atrium blood pool. ''Left.'' The atrium seen in the context of the thoracic vasculature. ''Right.'' Three patients from the Atrial Fibrillation DBP with particle correspondences (dots). The high degree of variability in shape and positions of the vessels and the appendage, which are considered important in fibrillation, present a severe challenge to the robustness of the system, and will require more sophisticated statistical models and user-defined landmarks.]] Clinicians in CARMA conduct over 20 ablations per week and see several times this number of patients in clinic, generating up to 50 separate MRI studies weekly, all of which require careful image processing and quantitative evaluation. All indicators suggest continued aggressive growth of this approach in our program and in many other clinics around the world, with CARMA at the center of a recently created multicenter clinical trial that will generate at least 50 additional scans per week. The urgent need to process and analyze such large volumes of real-world data in a timely manner will guarantee the widespread testing of tools developed within NA-MIC for this project.

The technical support for this DBP will be provided by Dr. Josh Cates, who recently received a PhD in Computer Science based on research in image processing and analysis. His mentor, Dr. Ross Whitaker, is PI of the Algorithms section of the Computer Science Core. Dr. Cates recently joined CARMA to assume leadership in the development, implementation, and validation of image analysis techniques and software. His expertise extends beyond algorithms to code development, as he was one of the original authors of ITK and is well acquainted with the software engineering practices and design ideas that also are shared within the NA-MIC Kit.
Dr. Rob MacLeod, a senior researcher in cardiac electrophysiology and biophysics, co-founder of CARMA, and associate director of SCI, is uniquely positioned to coordinate and direct this DBP.

==Connection to Cores and other DBPs==
This DBP will rely heavily on the Computer Science Core to identify specifically appropriate technical approaches and provide support for implementation and testing of new methods and workflows. The development efforts on pre- and post-therapy image registration, efficient segmentation of anatomical objects, including longitudinal processing, and work towards a patient scoring system for improved decision-making, will be shared with similar efforts of the other DBPs.

==Deliverable, Timeline, and Impact==

{|class="wikitable"
|-
|'''SPECIFIC AIMS'''
|'''Year 1'''
|'''Year 2'''
|'''Year 3'''
|-
|'''Aim 1'''
|Evaluate existing algorithms
|Integrate linear and nonlinear registration into prototype workflow
|Optimize tools, tests, and validation documentation
|-
|'''Aim 2'''
|Evaluate existing and implement new tools for atrial wall segmentation and for tissue characterization
|Joint segmentation of pre- and post-treatment data, efficient implementations through software and hardware acceleration
|Refine segmentation tools, tests and validation, integrate post-treatment image segmentation into clinical workflow, documentation
|-
|'''Aim 3'''
|Design of segmentation and registration workflow and application-specific GUI
|Prototype workflow system for integrated registration and segmentation, pre-/post-analysis and visualization. Tests on existing database.
|Tests on image data shared with other labs; establish database also with nonimage information for prototypical scoring system, training, and dissemination.
|}

Utah investigators will interact directly with NA-MIC PIs to develop new and refine existing algorithms for MRI-based registration and segmentation techniques to treat and manage AF. DBP activities at Utah will occur in consultation with the Lead NA-MIC Technical Contacts for Algorithms (Whitaker, Tannenbaum) and Engineering (Miller). Once completed and rigorously validated, these workflows will (1) be applied to the AF patient data described above, (2) made available for open dissemination via the NA-MIC website, and (3) form the basis for training and educational materials for NA-MIC investigators and the AF community. Results will be featured in presentations at scientific conferences, organized training events and workshops as a way to disseminate tool capabilities. Where possible, tutorials on how to use the NA-MIC technology for other AF-related projects will be made available. Finally, the DBP-PI will attend each NA-MIC All-Hands-Meeting to discuss the DBP with NA-MIC PIs, and report on new developments and progress. NA-MIC will benefit from this DBP by exposure to a rapidly growing clinical community involved with diagnosis, treatment, and management of AF patients. Even beyond AF, this DBP collaboration and associated clinical procedures and research, reaches a much wider clinical community involved with personalized medicine, which, given novel image processing tools, could better integrate newly emerging imaging into the current clinical workflow.

==References==
#Benjamin EJ, Levy D, Vaziri SM, D’Agostino RB, Belanger AJ, Wolf PA. Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. Jama. 1994;271(11):840-4. PMID: 8114238.
#Coyne KS, Paramore C, Grandy S, Mercader M, Reynolds M, Zimetbaum P. Assessing the direct costs of treating nonvalvular atrial fibrillation in the United States. Value Health. 2006;9(5):348-56. PMID: 16961553.
#Falk RH. Atrial fibrillation. N Engl J Med. 2001;344(14):1067-78. PMID: 11287978.
#Brodsky MA, Allen BJ, Walker CJ, 3rd, Casey TP, Luckett CR, Henry WL. Amiodarone for maintenance of sinus rhythm after conversion of atrial fibrillation in the setting of a dilated left atrium. Am J Cardiol. 1987;60(7):572-5. PMID: 3630939.
#Crijns HJ, Van Gelder IC, Van der Woude HJ, Grandjean JG, Tieleman RG, Brugemann J, et al. Efficacy of serial electrical cardioversion therapy in patients with chronic atrial fibrillation after valve replacement and implications for surgery to cure atrial fibrillation. Am J Cardiol. 1996;78(10):1140-4. PMID: 8914878.
#Van Gelder IC, Crijns HJ, Tieleman RG, Brugemann J, De Kam PJ, Gosselink AT, et al. Chronic atrial fibrillation. Success of serial cardioversion therapy and safety of oral anticoagulation. Arch Intern Med. 1996;156(22):2585-92. PMID: 8951302.
#Christophersen IE, Ravn LS, Budtz-Joergensen E, Skytthe A, Haunsoe S, Svendsen JH, et al. Familial aggregation of atrial fibrillation: a study in Danish twins. Circ Arrhythm Electrophysiol. 2009;2(4):378-83.
#Watanabe H, Darbar D, Kaiser DW, Jiramongkolchai K, Chopra S, Donahue BS, et al. Mutations in sodium channel beta1- and beta2-subunits associated with atrial fibrillation. Circ Arrhythm Electrophysiol. 2009;2(3):268-75.
#Wijffels MC, Kirchhof CJ, Dorland R, Allessie MA. Atrial fibrillation begets atrial fibrillation. A study in awake chronically instrumented goats. Circulation. 1995;92(7):1954-68. PMID: 7671380.
#Allessie MA, Boyden PA, Camm AJ, Kleber AG, Lab MJ, Legato MJ, et al. Pathophysiology and prevention of atrial fibrillation. Circulation. 2001;103(5):769-77. PMID: 11156892.
#McGann CJ, Kholmovski EG, Oakes RS, Blauer JJ, Daccarett M, Segerson N, et al. New magnetic resonance imaging-based method for defining the extent of left atrial wall injury after the ablation of atrial fibrillation. J Am Coll Cardiol. 2008;52(15):1263-71. PMID: 18926331.
#Badger TJ, Adjei-Poku YA, Marrouche NF. MRI in cardiac electrophysiology: the emerging role of delayed-enhancement MRI in atrial fibrillation ablation. Future Cardiol. 2009;5(1):63-70. PMID: 19371204.
#Badger TJ, Oakes RS, Daccarett M, Burgon NS, Akoum N, Fish EN, et al. Temporal left atrial lesion formation after ablation of atrial fibrillation. Heart Rhythm. 2009;6(2):161-8. 
#Oakes RS, Badger TJ, Kholmovski EG, Akoum N, Burgon NS, Fish EN, et al. Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation. 2009;119(13):1758-67. PMID: 19307477.
#Akoum N, Marrouche NF. Real-time imaging in electrophysiology: from intra-cardiac echo to real-time magnetic resonance imaging. Europace. 2009;11(5):539-40. PMID: 19359332. 
#Hauser TH, Peters DC, Wylie JV, Manning WJ. Evaluating the left atrium by magnetic resonance imaging. Europace. 2008;10 (Suppl 3):iii22-iii7. 
#Kolandaivelu A, Lardo AC, Halperin HR. Cardiovascular magnetic resonance guided electrophysiology studies. J Cardiovasc Magn Reson. 2009;11(1):21. PMID: 19580654. 
#Saremi F, Tafti M. The role of computed tomography and magnetic resonance imaging in ablation procedures for treatment of atrial fibrillation. Semin Ultrasound CT MR. 2009;30(2):125-56. PMID: 19358443. 
#Peters DC, Wylie JV, Hauser TH, Nezafat R, Han Y, Woo JJ, et al. Recurrence of atrial fibrillation correlates with the extent of post-procedural late gadolinium enhancement: a pilot study. JACC Cardiovasc Imaging. 2009;2(3):308-16. PMID: 19356576.
#Wylie JV, Jr., Peters DC, Essebag V, Manning WJ, Josephson ME, Hauser TH. Left atrial function and scar after catheter ablation of atrial fibrillation. Heart Rhythm. 2008;5(5):656-62. PMID: 18452866. 
#Krishnam MS, Tomasian A, Malik S, Singhal A, Sassani A, Laub G, et al. Three-dimensional imaging of pulmonary veins by a novel steady-state free-precession magnetic resonance angiography technique without the use of intravenous contrast agent: initial experience. Invest Radiol. 2009;44(8):447-53. PMID: 19561516.
#Chyou JY, Biviano A, Magno P, Garan H, Einstein AJ. Applications of computed tomography and magnetic resonance imaging in percutaneous ablation therapy for atrial fibrillation. J Interv Card Electrophysiol. 2009;26(1):47-57. PMID: 19521756.
#Rossillo A, Indiani S, Bonso A, Themistoclakis S, Corrado A, Raviele A. Novel ICE-guided registration strategy for integration of electroanatomical mapping with three-dimensional CT/MR images to guide catheter ablation of atrial fibrillation. J Cardiovasc Electrophysiol. 2009;20(4):374-8. PMID: 19017352.
#Tops LF, Schalij MJ, den Uijl DW, Abraham TP, Calkins H, Bax JJ. Image integration in catheter ablation of atrial fibrillation. Europace. 2008;10 (Suppl 3):iii48-iii56.
#Sra J, Ratnakumar S. Cardiac image registration of the left atrium and pulmonary veins. Heart Rhythm. 2008;5(4):609-17. PMID: 18325847.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

DBP:Autism

2016-02-09T04:32:33Z

Zack: Update from Wiki

=Autism Solutions=
{|class="wikitable"
|[[Image:Oguz-ISBI2008-fig1.png|600px]]
|
*'''Data:''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Data Brain: 2-4 Year Old from Autism Study]
*'''Tutorial:''' [http://wiki.na-mic.org/Wiki/index.php/Downloads#Tutorials ARCTIC: Automatic Cortical ThiCkness]
*'''Software for Slicer 3.6:''' [http://wiki.slicer.org/slicerWiki/index.php/Modules:ARCTIC-Documentation-3.6 ARCTIC]
*'''Representative Publication:''' [http://www.na-mic.org/publications/item/view/1444 1444]
*'''Final Report''' [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNCFinal:2010 UNC''']
|}

===Longitudinal MRI Study of Early Brain Development in Neuropsychiatric Disorder-Autism===
The primary goal of the University of North Carolina DBP is to learn more about autism by examining cortical thickness patterns in the early developing brain. Increasing evidence indicates that brain volume in children with autism is enlarged relative to normal controls. Whether these differences are due to increased cortical thickness or increased cortical surface area, however, is less clear. Studies of cortical growth during early brain development have been limited because existing tools for measuring brain volume are designed for the mature brain. A collaborating center of the NIH-funded Neuroimaging Study of Autism, clinical researchers at UNC already had acquired MRI data from a longitudinal sample of toddlers with autism, along with a comparison group of age and developmentally matched controls. NA-MIC had the inherent capability in 3D Slicer to produce cortical thickness measures for both individual and group analysis that could be developed for the pediatric population. Over the past several years, UNC has worked closely with NA-MIC computer scientists and software engineers to develop and deploy an end-to-end solution for measuring cortical thickness using data from MRI scans of toddler brains. This module, called '''ARCTIC''' (Automatic Regional Cortical ThiCkness) provides end-users with complete capability to perform individual regional cortical thickness analysis in the early developing brain.

[[Driving_Biological_Projects|Back to Driving Biological Projects]]

Service

2016-02-09T04:08:00Z

Zack: Update from Wiki

__NOTOC__
=Service=
'''PI: Will Schroeder, Ph.D., Kitware, Inc.'''
[[Image:Big-Service-Logo.png|150px|left]]
The Service core is responsible for the design and operation of the collaborative computing infrastructure that supports the research and outreach activities of the NA-MIC community. This infrastructure enables NA-MIC research to have a significant and lasting impact on the broader field of medical image analysis. The elements of this design are embodied in a community-based infrastructure that encourages research and development and promotes open science.

Community-based infrastructure, as its name implies, addresses the demands of developers and users while maintaining the ideals of the community. It enforces standards for coding style, documentation, and testing. Open science is achieved when the workproduct created by individual members or groups within the community is sufficiently documented and shared such that it can be replicated and used as a foundation for derivative work. The cost of participating in this open environment, however, must be minimized. When the burden of the infrastructure outweighs its benefits, the growth of the community and the spread of open science are hindered. The NA-MIC infrastructure offers clear benefits to developers and users while promoting the ideals of NA-MIC and open science, posing minimal burden on the community. The integrity of this infrastructure is maintained by our commitment to:
 
==Focus Areas==
#Maintain open licensing
#Support the delivery of quality software
#Support the development of useful algorithms
#Gather and respond to feedback from the community
#Facilitate documentation, software, and data-sharing

===Maintain Open Licensing===
Successful, community-based software requires the active involvement of developers and users with priorities that span the essential components of software development, .e.g., coding, algorithms, testing, and documentation. No single organization can meet all of those priorities with equal vigor: academics emphasize algorithms, programmers emphasize coding, teachers emphasize documentation/training, and industry emphasizes testing. The software must be distributed under a license that is suitable for both academics and industry. This is essential to attracting the diversity the platform needs to succeed.

The NA-MIC Kit is distributed under a BSD-style license, which permits royalty-free use of the NA-MIC Kit software and data for both academic and commercial applications. Licensing terms are clearly posted in the headers of the NA-MIC Kit code, in the text on NA-MIC websites, and in the “About” message of NA-MIC Kit applications. Code that infringes on patents or contains viral licenses is vigilantly avoided. All members of the NA-MIC community agree to be bound by the terms of the license for the good of the community and open science.

===Support the Delivery of Quality Software===
Large-scale, collaborative software development demands rigorous software processes to support the many activities that combine for effective software development. These include requirements generation, implementation, testing, documentation, distribution, and reports generation. Once established, these processes must be enforced. To maintain the integrity of the NA-MIC platform, we have identified champions, a common practice in large-scale software development, which have the authority to identify and correct deviations from standard practices. Our experience with VTK and ITK shows that novice developers occasionally resist standard practices because of the perceived coding overhead. Consequently, our infrastructure is designed to minimize the duration and magnitude of that overhead to encourage participation and ensure that the benefits of our software processes can be fully realized.

===Support the Delivery of Useful Algorithms===
NA-MIC facilitates the development of useful algorithms by fostering open communication and providing specialized infrastructure for algorithms validation, including distributed computing for parameter space explorations, algorithm explorations, algorithm comparisons, and longitudinal studies.

===Gather and Respond to Feedback===
Fostering communication early in the development process identifies synergies, avoids duplication of effort, and maintains a common design pattern. The key steps to guiding project development are: (1) share ideas, (2) collect votes from the community to establish which ideas are favored, and (3) respond, implement, and then repeat the process.

===Facilitate the Sharing of Documentation, Software, and Data===
Information sharing (software, documentation, data) is the foundation of NA-MIC and essential to accelerating the pace of research in the field of medical image analysis. This foundation is preserved by monitoring our communications infrastructure, supporting software modularity, holding open design discussions, hosting data repositories for use within and beyond NA-MIC, maintaining the PubDB repository of NA-MIC publications, and recognizing contributors. In all of these endeavors, acknowledging contributors is important to encouraging community involvement and a high priority for the Service core, which ensures that communication and publication channels carry appropriate acknowledgments for all contributors.

Overview

2016-02-09T04:07:59Z

Zack: Update from Wiki

__NOTOC__
=Overview=
==Delivering on the Promise of the Information Technology Revolution==
[[Image:Indesign_Fig.1-1.final_composite.png|400px|left|thumb|This image shows relevant structures from multiple modalities: '''1''' Surface of the white matter, '''2''' Surface of the gray matter, '''3''' Local U fibers, '''4''' Intraparenchymal bleed, '''5''' Part of the corticospinal tract. The image processing used to generate this result includes multi-modal image registration (T1, T1 postcontrast, T2, Flair, DTI); automated segmentation of brain tissue, CSF, lesions, and bleeding; user-guided tractography based on fiber clustering; and interactive integrated display.]]

NA-MIC, the National Alliance for Medical Image Computing, is a multi-institutional, interdisciplinary community of researchers, who share the recognition that modern healthcare demands improved technologies to ease suffering and prolong productive life. Organized under the National Centers for Biomedical Computing six years ago, NA-MIC was created to implement a robust and flexible open-source infrastructure for developing and applying advanced imaging technologies across a range of important biomedical research disciplines. A measure of its success, NA-MIC now is applying this technology to diseases that have immense impact on the duration and quality of life: cancer, heart disease, trauma, and degenerative genetic diseases. The target of this techology is shifting from whole populations to subject-specific analysis. Practicing clinicians and biomedical researchers are intimately aware of gaps in their ability to apply medical knowledge to the needs of individual patients. An abundance of electronic clinical data is produced over the course of an individual's disease progression and treatment, representing an enormous opportunity for improving medical care. The task of interpreting this mass of clinical data, however, has become correspondingly complex, often confounding this opportunity. To borrow a phrase from the intelligence community, the medical community is facing a challenge of "not enough eyeballs per pixel."

An example of subject-specific analysis on an individual dataset in a patient with moderate traumatic brain injury and intraparenchymal bleeding is shown in the Figure. These results were created using preliminary versions of novel methodologies and tools currently in development by NA-MIC. These methodologies and tools are being developed to aid the clinical assessment of damage and viable treatment options in TBI patients. Analysis of diffusion tensor images allows identification of white matter tracts in the vicinity of one of the lesions. Despite the complexity of the process, this image was constructed on a standard laptop in under 30 minutes. This sort of analysis will eventually enable clinicians to identify issues unique to the patient, guide treatment, and predict outcome in a clinically viable timeframe.

In practice, delivering on the promise of the information technology revolution in biomedical imaging is exceptionally difficult. The barriers to entry include the need to interoperate with scanners and other clinical systems, to organize and present clinical information according to accepted conventions, and to deploy computer systems that can efficiently process data within the time constraints of clinical practice. Since many of these requirements are common across a range of clinical applications, the NA-MIC open source platform allows the most labor intensive development and debugging tasks to be shared by the community for mutual benefit. A well defined, scalable software architecture and rigorous engineering methodology are essential to making software of this scale viable and are hallmarks of the NA-MIC approach.

==Impact through Collaboration==
NA-MIC has funded activities at multiple centers in the United States and participated in multiple international collaborations. Each center serves as the nexus for interaction between computer scientists and medical researchers investigating biological problems of vital importance to human health. In the accompanying schematic, each disease process, anatomic location, funding institution, and geographic location has a corresponding color code. Colon cancer, for example, is identified by a yellow dot. Six of these projects are funded by NIH institutes (MH, RR, EB, HL, CA, NS). One additional project is funded by the NSF.
[[Image:full_body_geoanatomy_international-illustration-05192010-800.png|full_body_geoanatomy_international-illustration-05192010-800.png|left]]

The scope of NA-MIC activities includes both the highly speculative exploration of new mathematical formations of core image analysis techniques and the ongoing effort of delivering and supporting binary distributions of software applications across a range of computing platforms. To address this continuum, the NA-MIC Computer Science Core is organized around two teams: Algorithms and Engineering. These teams bring complementary skills to the technical challenges posed by the driving biologic projects (DBPs). The Algorithms team is led by five senior investigators from four academic institutions. Their combined background provides expertise in variational, statistical, and geometrical approaches to image analysis. The Engineering team is led by five senior investigators from two small businesses, an industrial research facility, and two academic institutions. The joint output of these two teams is the NA-MIC Kit, which embodies a comprehensive set of analysis techniques in a well architected, documented, and widely used platform.

==Creating Tools and Infrastructure for Personalized Medicine==

NA-MIC is active in two fundamental aspects of biomedical technology: basic research to characterize diseases in populations and clinical decision support to translate this general knowledge to the needs of individual patients. This integration of approaches is referred to as ''personalized medicine.''

The first six years of NA-MIC were focused primarily on basic research in schizophrenia, autism, and lupus, where detailed individual analyses of lesion locations, cortical thickness, white matter architecture, and brain structure morphometry were studied in large populations. Statistics from these group comparisons are essential to defining the range of variation endemic in these conditions. As part of these efforts, NA-MIC invested heavily in the technologies for analysis and visualization of multimodal imaging studies of individuals. Increasingly these tools and approaches have supported translational science in support of patient-specific clinical decision support. Although our computer scientists have strong credentials in this area, NA-MIC's involvement in translational medicine began in earnest with our work in MRI-guided prostate cancer interventions through a funded DBP, in addition to funded collaborations on topics such as CT colonography, radiofrequency liver tumor ablation, and analysis of PET-CT studies of lung tumor characteristics. Our current DBPs reflect the goal of embodying knowledge gained from population analysis in new or modified software systems directed to the benefit of individual patients.

As the current NA-MIC DBPs make concrete technical advances to subject-specific analysis in their respective fields, these advances reflect and inspire important developments in the NA-MIC Kit, which enhance its utility as a research platform and lead to new software solutions. The current DBPs, for example, require improved robustness and efficiency of multimodal nonlinear image registration and segmentation for almost every medical image computing application. All of the DBPs work with multiple or longitudinal image data of individual subjects. These data are used to detect and characterize changes from baseline secondary to disease, trauma, or treatment. This effort requires novel image processing and visualization tools for qualitative and quantitative assessment of serial images. Quantitative evaluation of pathology, a main theme in all four DBPs, necessitates new types of efficient intuitive user-interaction in 2D and 3D displays to efficiently initialize and guide image registration and segmentation procedures. To adapt these tools for clinical use, refinements are needed to render the NA-MIC Kit more compatible with clinical systems, including capabilities for representing clinical data, PACS interfaces, and DICOM networking. Another technical goal common to all NA-MIC DBPs is the delivery of integrated solutions of data, software, and tutorials that represent clinical Best Practices. In this regard, the Computer Science Core is creating or modifying infrastructure for optimizing and validating these Best Practices, while the Service, Training, and Dissemination Cores are designing systems and methodologies for effectively communicating these Best Practices to the wider research community.

To meet the challenges of the information technology revolution, the NA-MIC organization is guided by three principles: (1) Innovation cannot and should not be managed from above, (2) Communication and training across computer science and clinical research disciplines is critical to providing the highest quality biomedical image analysis research, and (3) Open science is good science. These principles are applied in a culture of shared decision-making and mutual responsibility among Core PIs. They have served our Center well in the past and will continue in the future.

News-CMake

2016-02-09T04:07:46Z

Zack: Update from Wiki

==The NAMIC-Kit Goes Global==

[[image:CMake-logo-med-res.png|200px|left]]

On January 11, 2008 [http://kde.org/announcements/4.0/ KDE 4.0.0] was officially released . This is the next cutting-edge version of KDE, consisting of the KDE
libraries, the workspace (desktop, start panel, window manager), and applications (such as kOffice, kDevelop, etc.). One of the major features of KDE 4 is
that it now runs natively on Mac OSX and on Windows, beyond the previous Linux platforms. Such cross-platform support is now possible
in major part because KDE is now built using CMake, the cross-platform build, test, package and software process tool, and a part of the [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit NAMIC Kit].

For more information, see the [http://video.google.com/videoplay?docid=6642148224800885420&hl=en video]. Here you can watch the keynote of the official KDE 4 release event, demos of the many components of KDE4, and as well as live demos on Mac OS X (CMake is mentioned at this point) and on Windows.

NA-MIC-Kit

2016-02-09T04:07:45Z

Zack: Update from Wiki

The NA-MIC kit is ....

Management

2016-02-09T04:07:44Z

Zack: Update from Wiki

{{Projects/NAMICWeb}}
=='''Management'''==
[[Image:Big-Management-Logo.png|150px|left]]
The specific aim of Core 7 is to provide a flexible yet effective administrative structure for managing the many collaborative activities of the 3 technology and 4 service cores of NA-MIC. To accomplish this aim, Core 7 will develop and execute a management plan based on a balanced management strategy that accommodates shared decision-making and mutual responsibility among the core PIs, while providing oversight and leadership in support of quality biomedical computing and investigation. The management structure of NA-MIC can be defined as the implementation of a balanced management strategy in the context of a “distributed” center with distributed decision-making and distributed responsibilities among the participating cores. The management structure defines a clear role for each core and key investigator and consists of a Project Management Office, an External Advisory Committee, a Governance Committee, and several Special Topic Task Forces to address special issues. The Core 7 Research Plan provides an organizational chart of NA-MIC, including the names of the investigators (i.e., Chairs and PIs) responsible for each unit.

Consistent with the concept of a distributed center, the individual cores of NA-MIC have the autonomy and knowledge to plan and manage their own resources and activities. The role of the NA-MIC Project Management Office is to provide overall project planning, integrated project tracking, and oversight for quality assurance of deliverables from individual cores. The External Advisory Committee (EAC) consists of external representatives from the NIH funding agency, scientific and clinical user community, and biomedical imaging and computational scientists nationwide. This committee will meet on an annual basis to review progress and offer constructive advice to the PIs and investigators of the Center. The Governance Committee is responsible for establishing and executing NA-MIC policies, setting scientific priorities, resolving potential resource conflicts, reviewing and approving research and collaborative projects of NA-MIC, and has final responsibility for all fiscal matters. Each Task Force is charged with the duty of developing detailed NA-MIC Governance Policies and Operational Guidelines of a subject-specific nature, e.g., intellectual property, governance, data-sharing, and training. Task forces will be created and disbanded by the Governance Committee and exist for specified periods of time.

Having the right organizational structure in place is only the first step to ensuring the team environment that will permit NA-MIC to achieve its goals. The distributed nature of this center requires close interaction between the various research and support cores. The essence of teamwork is a common commitment to a common set of goals. To achieve this goal, we have defined the second step of the NA-MIC management approach to work-product development, which can be viewed in terms of the spiral model and iterative process that cultivates and reinforces the interaction and collaboration of various teams under the NA-MIC management structure. Core 7 also presents a strategy for the recruitment of new collaborations and DBPs in Year 4 of the project. Finally, the management plan includes a summary of the major milestones and activities this alliance hopes to accomplish.

The following illustration demonstrates how the NA-MIC Cores interact (click image for larger view):

[[Image:CoreRelations.png|699px]]

Main Page

2016-02-09T04:07:43Z

Zack: Update from Wiki

{{Projects/NAMICWeb}}

The National Alliance for Medical Imaging Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the center is to provide the infrastructure and environment for the development of computational algorithms and open source technologies, and then oversee the training and dissemination of these tools to the medical research community. This world-class software and development environment serves as a foundation for accelerating the development and deployment of computational tools that are readily accessible to the medical research community. The team combines cutting-edge computer vision research (to create medical imaging analysis algorithms) with state of the art software engineering techniques (based on "extreme" programming techniques in a distributed, open-source environment) to enable computational examination of both basic neurosience and neurological disorders. In developing this infrastructure resource, the team will significantly expand upon proven open systems technology and platforms.

The driving biological projects will come initially from the study of schizophrenia, but the methods will be applicable to many other diseases. The computational tools and open systems technologies and platforms developed by NA-MIC will initially be used to study anatomical structures and connectivity patterns in the brain, derangements of which have long been thought to play a role in the etiology of schizophrenia. The overall analysis will occur at a range of scales, and will occur across a range of modalities including diffusion MRI, quantitative EGG, and metabolic and receptor PET, but potentially including microscopic, genomic, and other image data. It will apply to image data from individual patients,and to studies executed across large poplulations. The data will be taken from subjects across a wide range of time scales and ultimately apply to a broad range of diseases in a broad range of organs.

'''<center>Supported by the National Institutes of Health, Roadmap Initiative for Bioinformatics and Computational Biology.'''
'''For more information, see http://www.bisti.nih.gov/ncbc'''

Day-to-day organization of NA-MIC is done using http://wiki.na-mic.org.

Information about collaborating with NA-MIC is available at this information page on our wiki.</center>

Links

2016-02-09T04:07:42Z

Zack: Update from Wiki

=Links=
<gallery Caption="NA-MIC interacts with and builds upon a number of projects" widths="100px" heights="75px" perrow="5">
image:logo_spl.gif|[http://spl.harvard.edu Surgical Planning Laboratory] (SPL)
image:logo_nac.gif|[http://nac.spl.harvard.edu Neuroimage Analysis Center] (NAC)
image:logo_igt.gif|[http://www.ncigt.org National Center for Image Guided Computing] (NCIGT)
image:LogoBIRN.jpg|[http://www.nbirn.net The Biomedical Informatics Research Network] (BIRN)
image:Logo-ITK.png|[http://www.itk.org The Insight Consortium]
</gallery>

Leadership

2016-02-09T04:07:41Z

Zack: Update from Wiki

{{Projects/NAMICWeb}}
==Introduction==
The function of the leadership core is to provide overall leadership to the NA-MIC effort by coordinating between the different cores, facilitating consensus formation and execution of plans agreed to. In addition to scientific leadership of NA-MIC, the leadership core works with NIH officers and external collaborators and coordinates the outreach activities. The principal investigator, Ron Kikinis, works closely with the Core PI's, the DBP's and the outreach cores to accomplish these tasks.

== Contact ==

For more information about NA-MIC, please contact:

Ron Kikinis, M.D. 
Surgical Planning Laboratory 
Brigham and Women's Hospital 
1249 Boylston St., Room 352 
Boston, MA 02215

'''Phone:''' +1 617.732.7389 
'''E-mail:''' kikinis at bwh.harvard.edu

Gallery

2016-02-09T04:07:36Z

Zack: Update from Wiki

{{Projects/NAMICWeb}}
=='''Image Gallery'''==

[[Image:park.png|thumb|500px|left|Upper left panel displays fiber tractography combined with cortical thickness map obtained with Free Surfer. Upper right panel demonstrates a sagittal cross-sectional view of brain parenchyma segmented into white and gray matter combined with fiber tractography map. Lower left figure shows a coronal cross-sectional view of automatic parcellation of white matter overplayed with the gray matter surface. Lower right figure shows the parcellation of gray matter surface and corresponding white matter fibers.]]

 

[[Image:gerig.png|thumb|500px|left| Visualization of variation in ventricle volume for pairs of subjects (MZ – monozygotic twins, DS – monozygotic twins discordant for schizophrenia, DZ – dizygotic twins, NR – nonrelated pairs). The distances are color-coded to show absolute differences between 2 and 8mm. The figures illustrate decreasing shape similarity MZ = DS < DZ < NR. Healthy MZ are not significantly different from MZ discordant for schizophrenia (DS).]]

 

[[Image:mgh.png|thumb|500px|left| DTI fractional anisotropy (FA) map on computationally inflated cortical surface. The cortical surface FA map indicates the microstructural integrity of the white matter 2mm subcortical to the white matter-gray matter interface.]]

 

[[Image:mit.png|thumb|500px|left| Principal deformation for the right hippocampus for normal controls (top) and schizophrenia patients (bottom). Four views (front, lateral, back, medial) of each shape are shown. The color indicates the direction and the magnitude of the deformation, changing from blue (inwards) to green (no deformation) to red (outwards).]]

Engineering

2016-02-09T04:07:35Z

Zack: Update from Wiki

__NOTOC__
=Engineering=
[[Image:Big-Engineering-Logo.png|150px|left]]

The investigators of the Engineering team have demonstrated expertise in visualization, medical image analysis, information systems, and computing platforms, and have a long track record of developing and delivering software platforms, computing platforms, and software engineering to large multisite research programs (Visible Human Project, BIRN, NCRR CTSA, SimBios, Oasis, Jackson Heart Study, IMAGEN, INTRUST, Neuroimaging Analysis Center, National Center for Image Guided Therapy, National Center for Microscopy and Imaging Research, MedINRIA).

The objective of the Engineering component of the Computer Science Core is to provide software tools and software development processes to deploy innovative technology to clinical researchers, support the scientific algorithm innovation of the Algorithm scientists, and to foster a community to produce high quality software. This objective is consistent with the original objective of the Engineering Core from the first NA-MIC funding period. However, the specific aims through which we will address this objective in the next NA-MIC funding period reflect the maturation of the NA-MIC community, the capabilities of the current 3D Slicer platform and NA-MIC Kit, and the changing needs of the Algorithm team and DBPs as NA-MIC pursues personalized medicine through the patient-specific analysis of images. Specifically, these aims are: (1) Architecture; (2) End-user platform; (3) Computational platform; (4) Data management platform; (5) and Software engineering and software quality.

==Architecture==
[[Image:Indesign_Fig._1-2.Final.png|500px|left|thumb|Figure: NA-MIC Kit. Algorithm developers contribute to the computational platform (image analysis: ITK and Teem; visualization: VTK and OpenCL) and application developers create tools within an architectural framework (scene graph: MRML, GUI: Qt, scientific computing: Python) in conjunction with data management facilities (XNAT) and under the control of the quality software process (CMake and CDash). The 3D Slicer platform is designed to accommodate accelerated innovation with a flexible execution engine on which community-developed analysis modules can be rapidly deployed to clinical researchers and the broader community via the 3D Slicer.]]

We will expand the architecture of the NA-MIC Kit by defining the components and interfaces that support the requirements of the Algorithms team and the DBPs, and by addressing, in particular, workflows for registration; interactive methods for quick and accurate delineation of pathology boundaries; rich descriptors of size, structure, and function for regions of interest (ROIs); methods for multivariate statistical analysis; and interfaces to clinical data resources. In this renewal, the DBPs’ focus on patient-specific analysis places new demands on the architecture as imagery from multiple time points and multiple modalities must be analyzed to understand the extent of the disease or injury and to quantify change. These needs, in turn, require new data structures for managing multivariate time-series data, new interfaces to statistical libraries, and new components for interactive analysis methods that leverage accessible computing resources, e.g., GPUs and cloud computing.

==End-user platform==
We will extend the 3D Slicer application to enhance the transition of innovative algorithms into clinical research applications. In particular, we will work on: expert-guided interactive segmentation tools to define and edit anatomical atlases; general purpose plotting, graphing, and information visualization to display complex data and the results of batch processing; multimodal and time series visualization tools for tracking disease progression and treatment response and for comparing analysis results across a range of parameter settings; and uniform application support for nonlinear and piecewise spatial transforms to accommodate new registration algorithms. To accommodate this functionality, we will expand the current data structure support for multiple volumes, surface models, transformations, and annotations to include finite element meshes, diffusion tensor fiber bundles, and vascular network representations; and enable advanced analysis of these types through the 3D Slicer’s powerful, run-time extensible execution model.

==Computational platform==
We will expand the NA-MIC Kit to support advances in computing methodology and analysis techniques. To make longitudinal data analysis, interactive segmentation and registration,
and information visualization algorithms broadly accessible to developers and users, the toolkit will support cloud computing and stream (GPGPU) processing, and will integrate VTK’s new information visualization and informatics subsystem into the NA-MIC Kit. Further, we will integrate feature measurement libraries, multivariate clustering techniques, and machine-learning libraries; and provide statistical inference capabilities with regression analysis and interfaces to statistical packages such as R. These capabilities will be building blocks
for the algorithm developers and will provide tools accessible to the end-user through the 3D Slicer.

==Data management platform==
We will create a platform that enables users to readily store, process, exchange, and manage data. The research activity of the DBPs, and the NA-MIC community as a whole, necessarily
involves a large quantity of imagery, potentially multimodal and longitudinal. The proposed platform will enable users to exchange data with clinical devices, to interact with central research data repositories, and to manage local data caches. The platform will be based on the XNAT imaging informatics system which is already interoperable with the 3D Slicer. The platform will include a database component to store imaging data and metadata, a desktop application to organize and explore data, and a network and programming interface to exchange data with external applications, including clinical information systems and image review workstations.

A major focus of development will be providing deep support for the DICOM medical imaging industry standard, which will enable seamless integration with clinical devices, including scanners, PACS, and image-guided surgery systems.

==Software engineering and software quality==
We will extend our current software engineering and software quality infrastructure to provide rigorous traceability and control across project history. This necessitates a software development infrastructure that tightly integrates software revision control, build, test, documentation, and release into a continuous, self-documenting process. By extending our widely used CMake system for cross-platform build; CDash/CTest for testing; and CPack for cross-platform packaging and distribution, we will facilitate our goals of deploying the NA-MIC Kit to the broad research community and enable the community to contribute back to the toolkit. Further, this integrative infrastructure will have many benefits. Notably, this integration will provide a centralized documentation of a project’s entire life cycle. This will aid collaborative design, development and debugging, and will position the software for deployment under regulatory control.

<gallery Caption="Engineering Core Members" widths="120px" heights="75px" perrow="6">
image:KitwareLogo2.jpg|Will Schroeder, [http://www.Kitware.com Kitware], Clifton Park, NY
image:Isomics logo.png|Steve Pieper, [http://www.isomics.com Isomics], Cambridge, MA
image:GE logo.jpg|Jim Miller, [http://www.ge.com/research/ GE Global Research], Niskayuna, NY
image:LogoBIRN.jpg|Mark Ellisman, Jeff Grethe, [http://www.nbirn.net/ BIRN], UCSD
image:Nrg.gif|Daniel Marcus, [http://nrg.wustl.edu/ NRG], WUSTL, St. Louis, MO
</gallery>

DBP:Huntington's Disease

2016-02-09T04:07:26Z

Zack: Update from Wiki

__NOTOC__
=HUNTINGTON’S DISEASE=
'''PI: Hans Johnson, Iowa University'''

==Specific Aims==
The NIH-funded project “Neurobiological Predictors of Huntington’s Disease” (PREDICT-HD) is studying Huntington’s disease (HD), a neurodegenerative genetic disorder that affects muscle coordination, behavior, and cognitive function, and causes severe debilitating symptoms by middle age. The aims of this DBP capitalize on two unique aspects of HD among neurodegenerative disorders, namely, the ability to know in advance exactly who will develop the disease and the knowledge that all affected individuals have the same root cause (i.e., a CAG repeat expansion in the huntingtin gene).

'''1. Perform individualized longitudinal shape change quantifi cation from multimodal data.'''
Morphometric brain differences begin 15 years or more before the symptoms of HD become debilitating. The ultimate goal of the PREDICT-HD study is to sufficiently define the neurobiological progression of HD in at-risk individuals so that clinical trials of potential disease-modifying therapies can be performed before symptoms reach a debilitating stage. Previous cross-sectional group analyses show that just before patients manifest symptoms, the caudate and putamen are severely affected [1] and there are widespread changes in cortical thickness [2, 3]. The NA-MIC multi-subject single modality group-wise registration methods will be modified to perform single subject multimodality longitudinal registrations.

'''2. Perform full brain diffusion tensor imaging tractography analysis.'''
In addition to morphometric gray matter measurements, diffusion tensor imaging (DTI) fractional anisotropy measurements indicate that white matter changes occur very early in HD [3-5]. The development of tools for DTI data hold great promise for identifying early disease markers suitable for measuring longitudinal trajectory changes over short time intervals. Tools for segmenting white matter based on the high-resolution 3T multimodal scans plus DTI data consistently identify white matter anatomical regions. Methods for white matter
fiber tracking from DTI data can identify anatomically connected regions within a single subject. Longitudinal analysis of white matter changes will help identify cause/effect relationships of disease progression between cortex, white matter, and sub-cortical connected regions.

'''3. Deploy extensible tools for sharing source data, derived data, algorithms, and methods to multi-site analysis teams.''' It is widely recognized that the PREDICT-HD imaging dataset has extraordinary value. It is further enriched through quality assurance documentation and integration with clinical measures. There is a great need to deploy mechanisms that facilitate external collaboration by providing: (1) data transfer methods for both raw scanner data and derived data, (2) access and interfaces to a wide variety of inter-connected image-processing
algorithms from other institutions, (3) procedures for integration of externally generated derived datasets with the central repository, and (4) training material on how to best use the datasets.

==Background==
The PREDICT-HD (5 R01 NS04006) study is an international 30-site observational study of longitudinal neurodegeneration of persons at-risk for HD with continuous funding from 2001 to 2013. PREDICT-HD has fulfilled all aims from its initial award and has become part of a world-wide effort to provide treatments for HD, both symptomatic and presymptomatic (“premanifest”). The PREDICT-HD cohort and database have become international resources and offer an unprecedented opportunity to examine the pathophysiology and neurobiology of early HD. The specific short-term aims of PREDICT-HD are: (1) to refine the prediction of disease
diagnosis (motor conversion) using longitudinal measures of plasma, imaging, cognitive performances, motor ratings, and psychiatric measures, and (2) to identify and characterize the natural history of sensitive markers of disease onset and progression that become abnormal prior to clinical diagnosis.

[[Image:DBP.HD1.png|500px|left|thumb|Figure 1. '''A, B''' Relationship between estimated years to diagnosis of Huntington's disease and motor exam score and striatal volumes. '''C''' Distribution of age of onset for individuals with 36-56 CAG repeats based on the parametric model. ''Red'' indicates most likely time of diagnosis. ''Blue'' line is porposed time period when interventional therapies would have greatest impact.]]The curves in panels A and B of Figure 1 show a cross-sectional analysis of HD subjects, where the red boxes represent the most likely time of neurological diagnosis and the blue boxes represent the proposed window for starting a disease-modifying intervention [6]. A well-established parametric survival model [7], based on CAG repeat length, predicts the probability of observed debilitating motor neurological symptoms. This is the current basis for disease onset at different ages of individual patients. A graphical depiction of this “onset” model is shown in Figure 1C, where the red line indicates the most likely age at which a neurological diagnosis will be made.

The cross-sectional analyses that led to the results shown in Figure 1 are informative for identifying the general progression of disease, but individualized longitudinal analysis is
needed to identify the appropriate time to start interventional treatment in the individual patient. The focus of this proposal is to improve the longitudinal analysis methods we currently use to include informed intervention criteria suitable for application decades before debilitating neurological symptoms manifest.

The PREDICT-HD study is currently collaborating with pharmaceutical companies to develop a promising long-term (3-10 year) therapeutic treatment for HD that involves a permanent implantable infusion pump for drug delivery. The NA-MIC Kit will contribute several technical aspects: (1) Precise subject-specific morphological mapping of white matter and gray matter sub-cortical regions will assist in developing the necessary masstransport models needed to optimize pump placement. (2) Longitudinal analysis of a single subject will inform clinicians of the most appropriate time for clinical intervention. (3) Finally, precision anatomical labeling will aid surgical planning for implantation.

==Investigators==

{|class="wikitable"
|-
|'''NAME'''
|'''DEGREE'''
|'''INSTITUTION'''
|'''EXPERIENCE'''
|'''ROLE'''
|'''URL'''
|-
|HANS JOHNSON
|PH.D.
|UNIVERSITY OF IOWA
|MEDICAL IMAGE PROCESSING, COMPUTER ENGINEERING & IMAGING INFORMATICS
|DBP PI
|http://www.psychiatry.uiowa.edu/mhcrc/IPLpages/IPL_postdoc.html
|-
|JANE S. PAULSEN
|PH.D.
|UNIVERSITY OF IOWA
|NEUROPSYCHOLOGIST
|PREDICT-HD PI
|http://www.uihealthcare.com/depts/huntingtonsdisease/staff/janepaulsen.html
|-
|VINCENT MAGNOTTA
|PH.D.
|UNIVERSITY OF IOWA
|DIFFUSION TENSOR IMAGING, RADIOLOGY
|CONSULTANT FOR DTI PROCESSING
|http://www.medicine.uiowa.edu/Radiology/faculty-staff/faculty/magnotta-vincent.html
|-
|KENT WILLIAMS
|M.S.
|UNIVERSITY OF IOWA
|SOFTWARE ENGINEERING, NA-MIC DEVELOPMENT BEST PRACTICES
|DBP SOFTWARE ENGINEER
|
|-
|DAN MARCUS
|PH.D.
|WASHINGTON UNIVERSITY, ST. LOUIS
|NEUROIMAGING INFORMATICS
|LEAD INFORMATICS & DATA DISSEMINATION
|http://www.mir.wustl.edu/research/physician2.asp?PhysNum=45
|-
|MARTIN STYNER
|PH.D.
|UNIVERSITY OF NORTH CAROLINA
|COMPUTER SCIENCE
|LEAD ALGORITHM & ENGINEERING CONTACT
|http://www.cs.unc.edu/~styner/
|}

==Methods==

'''Aim 1.''' To analyze longitudinal shape change, we will use a set of 80 subjects with between 3 and 6 longitudinal multimodal image sets (T1/T2/PD) and manually validated caudate, putamen, and thalamus segmentations to perform longitudinal registrations (i.e., not pairwise). This NA-MIC method will include provisions for addressing missing modalities. The proposed analysis will be used to provide robust estimates of longitudinal change. Additionally, the current NA-MIC shape analysis tools will be used to analyze a set of 225 subjects with between 3 and 6 T1 only scans and manually validated sub-cortical segmentations. These shape analyses will be used to create a normative model. In this way, changes in an individual’s scores can be used to inform clinical counseling and intervention scheduling decades before a neurological motor diagnosis is made.

The NA-MIC Kit 3D Slicer “Change Tracker” wizard provides functionality for investigating small longitudinal changes in pairwise bright object meningioma growth analysis. The “Change Tracker” is an exceptional prototype for the family of tools needed to analyze gray matter subcortical structure atrophy rates as a disease state marker. Development of the “Change Tracker” tools will be performed to generalize the tool for monitoring changes to subcortical brain structures.

'''Aim 2.''' The development of DTI quality control and atlas-building tools suitable for longitudinal white matter change quantification will be greatly accelerated by leveraging the existing NA-MIC expertise in DTI data processing. An HD-specific atlas will be constructed from a set 25 subjects with longitudinal 3T imaging data containing 2 high resolution T1, a high resolution T2, a 32 direction DTI sequence, and manually validated brain segmentations. We will collaborate with NA-MIC developers to create tools for a longitudinal analysis
pipeline of changes measured by fiber tractography to identify white matter tracts that have strong co-morbid degenerative timelines compared to subcortical degeneration over time. We will use the same data for whole brain longitudinal analysis of the DTI connectivity using stochastic tractography tools for network and pathology detection. Customized user interfaces will extend the diffusion tractography visualization modules in the 3D Slicer to target reporting of longitudinal fiber tracking results to the clinical audience.

'''Aim 3.''' To accomplish the dissemination and collaboration goals, we will deploy the XNAT environment. This effort will include quality-assurance procedures, data-processing tools, and a common knowledge base accessible to the extended HD community of image-processing experts. The NA-MIC data-sharing tools will be extended to facilitate the dissemination of raw scan data, derived image datasets, and measurement scores for Aims 1 and 2. The existing morphometric analysis pipelines used to create the manually validated segmentations
also will be incorporated into the XNAT processing pipeline.

==Connections between this work and any of the other 3 proposed DBPs==
The University of Iowa investigators have been long-term active participants in the development process for several NA-MIC Kit resources including ITK, XNAT, and 3D Slicer. The specific development needs of this DBP are consistent with many of the needs stated in the other DBPs. All of the projects have a need for robust well documented standardized workflows, longitudinal registration, and robust segmentation of anatomical regions. The HD, TBI, and RT for head and neck cancer DBPs share a common need for sensitive shape change measurement from longitudinal scans. Finally, the HD and TBI DBPs have a need for improved multi-model data analysis including quality control and analysis tools for investigating longitudinal white matter changes from diffusion tensor imaging.

==Deliverables, Timeline, Impact==
{|class="wikitable"
|-
|'''SPECIFIC AIMS'''
|'''Year 1'''
|'''Year 2'''
|'''Year 3'''
|-
|'''Aim 1'''
|Preliminary tools for longitudinal shape change applied to existing sets of segmented subcortical structures
|Improve shape analysis tools and apply to larger cohort with multiple study visits
|Create normative models of shape change in healthy aging and disease (HD)
|-
|'''Aim 2'''
||Quality control pipeline of DTI datasets. Test with preliminary tool for longitudinal analysis of white matter tracts
|Put refined tool for longitudinal analysis of fiber tracts into workflow.
Application to subjects with apparent pathologic changes
|Develop workflow of optimized longitudinal white matter analysis for whole brain tractography
|-
|'''Aim 3'''
|Stand up XNAT instance for PREDICT-HD project, customize to manage all expected data types.
Import existing data for internal use and testing
|Develop and incorporate PREDICT-HD specific workflows developed in Aims 1 and 2 into XNAT
|Documentation of workflows with training materials. Enable sharing with the scientific community as dictated by the PREDICT-HD project.
|}

The Iowa investigators will collaborate with the Computer Science Core to develop new and refine existing tools to achieve the specific aims of the HD-DBP. This effort will include participation in the two All-Hands Meetings each year, as well as generation of presentations for scientific conferences and peer-reviewed publications. When mutually beneficial, tool development will be coordinated with other DBPs. Anonymized versions of the imaging datasets necessary to achieve the stated aims of this DBP will be made available to the entire NA-MIC community to facilitate algorithm, workflow, training, and documentation efforts. The developed tools will follow the software engineering Best Practices guidelines established by NA-MIC and will be made available through the Service and Dissemination Cores as well as the [http://www.nitrc.org Neuroimaging Informatics Tools and Resources Clearinghouse]. In the second and third years of the project, training events will be held to coincide with the [http://www.euro-hd.net Annual European Huntington’s Disease Network Meeting], and the [http://www.humanbrainmapping.org Annual Human Brain Mapping Conference] to expose the larger HD community to the NA-MIC resources. An XNAT instance will be deployed with the entire deidentified PREDICT-HD imaging dataset, representative standard workflows, and links to the clinical variables in the [http://www.ncbi.nlm.nih.gov/gap dbGaP] will be made available to external researchers through a standard approval process. The NA-MIC efforts will facilitate our needs to: (1) integrate data from multiple protocols for generating a set of measures that can be studied to explore the longitudinal inter-relationships between known areas of degeneration, (2) disseminate a well documented set of best practices and training events for the HD imaging community to empower collaboration, (3) deploy a common centralized data-sharing infrastructure (i.e., XNAT) that is well integrated with the training software and development practices necessary to gain access to new imaging methodologies.

==References==
#Paulsen JS, Magnotta VA, Mikos AE, Paulson HL, Penziner E, Andreasen NC, et al. Brain structure in preclinical Huntington’s disease. Biol Psychiatry. 2006;59(1):57-6. PMID: 16112655.
#Nopoulos P, Magnotta VA, Mikos A, Paulson H, Andreasen NC, Paulsen JS. Morphology of the cerebral cortex in preclinical Huntington’s disease. Am J Psychiatry. 2007;164(9):1428-34. PMID: 17728429.
#Rosas HD, Hevelone ND, Zaleta AK, Greve DN, Salat DH, Fischl B. Regional cortical thinning in preclinical Huntington disease and its relationship to cognition. Neurology. 2005;65(5):745-7. PMID: 16157910.
#Reading SA, Yassa MA, Bakker A, Dziorny AC, Gourley LM, Yallapragada V, et al. Regional white matter change in pre-symptomatic Huntington’s disease: a diffusion tensor imaging study. Psychiatry Res. 2005;140(1):55-62. PMID: 16199141.
#Beglinger LJ, Nopoulos PC, Jorge RE, Langbehn DR, Mikos AE, Moser DJ, et al. White matter volume and cognitive dysfunction in early Huntington’s disease. Cogn Behav Neurol. 2005;18(2):102-7. PMID:15970729.
#Paulsen JS, Langbehn DR, Stout JC, Aylward E, Ross C, A, Nance M, et al. Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J Neurol Neurosurg Psychiatry. 2008;79:874-80. PMID: 18096682.
#Langbehn DR, Brinkman RR, Falush D, Paulsen JS, Hayden MR. A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length. Clin Genet. 2004;65(4):267-77. PMID: 15025718.

CollabGrants

2016-02-09T03:58:56Z

Zack: Update from Wiki

==Collaboration Grants==

{|class="wikitable"
|[[Image:Collab-composite-i.png|400 px|link=http://www.na-mic.org/Wiki/index.php/NA-MIC_External_Collaborations]]
|One way to measure a Center's technological success is to value its ability to attract external collaborations. As NA-MIC has evolved, the number of external collaborators seeking to use 3D Slicer to provide answers and solve problems in biomedical research has expanded steadily. NA-MIC currently has 30 active collaborations funded by NIH or other international institutions. The breakdown of current collaboration grants includes 8 PARs funded through the Program for Collaborations with National Centers for Biomedical Computing, 17 additional external collaborations representing a variety of national health institutes and funding mechanisms (e.g., R01, U54, PAR, etc.), and 5 international collaborations. The quality of the research institutions from which these collaborations emanate speaks volumes about the quality of 3D Slicer and the ability of NA-MIC scientists to deliver affordable, workable end-to-end solutions. Moreover, as the tools, technology, and infrastructure developed by NA-MIC have matured, so has the nature of these collaborations, now including organs, diseases, and disciplines well beyond the neuroscience field. Follow this [http://www.na-mic.org/Wiki/index.php/NA-MIC_External_Collaborations link] for a synopsis of NA-MIC's External Collaborations.
|}

Algorithms

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Zack: Update from Wiki

__NOTOC__

=Algorithms=

[[Image:Big-Algorithm-Logo.png|150px|left]]
The NA-MIC Algorithm scientists are responsible for pushing the boundaries of applied mathematical techniques in the context of the challenges of the Center's driving biological projects(DBPs). Their effort is currently focused on personalized medicine or patient-specific analysis of images. The goal is to address clinical problems that entail sequences of images from individuals with pathologies that deviate from normal population datasets. These applications are characterized by images that vary significantly from one patient to another, or from one time point to another, in ways that present distinct challenges to the current state-of-art algorithms used for image analysis. The clinical applications of the DBPs reflect this emphasis on individually distinct anatomy, pathology, and function. The importance of this new emphasis for the Algorithm effort is best understood in the context of the current technology for medical image analysis. Although image analysis tools are based on quite diverse methodologies, the most widely used methods rely on a large degree of anatomical similarity. For instance, tools for brain image analysis lean heavily upon the geometric regularity and stability of brain anatomy and function. Tools for image-guided therapy rely on carefully configured or engineered environments. This regularity or predictability allows prior knowledge, encoded as either a set of rules or population statistics, to constrain the problem and enable effective interpretation of images.

While these applications are important, much of medical practice has not benefited from the associated advances in medical image analysis. Indeed, most clinical practice is concerned with the treatment of patients who are either injured or exhibit a pathology that, when imaged, does not present itself in the highly predictable manner assumed by many analysis methods. The aim of the Algorithms team is to address a new set of technical challenges that represent opportunities to advance image analysis tools to impact a broader spectrum of applications in clinical practice. These include (1) Statistical models of anatomy and pathology, (2) Geometric correspondence, (3) User interactive tools for segmentation, and (4) Longitudinal and time-series analysis.

==Statistical models of anatomy and pathology==
We are currently developing technologies for representing and applying statistical models to capture a wider range of anatomies and pathologies than is currently considered by state-of-art technologies in image analysis. Such statistical models play an important role in virtually all types of advanced algorithms in medical image analysis. However, these methods rely on relatively simple, parametric distributions. Unfortunately, traditional parametric models cannot capture large, inherently nonlinear anatomical variations in heterogeneous populations. For instance, the changes in the surrounding anatomy induced by a tumor or positions of organs in a highly deformable anatomy, such as the abdomen, cannot be
represented as small, continuous deviations from a mean. More sophisticated statistical models are needed to adequately address problems in personalized medicine. Over the next several years our research in statistical modeling from images will be used to produce practical algorithms directly relevant to the clinical problems of the current DBPs. Specifically, we will develop models that can handle (1) changes in heart images that result from fibrosis and remodeling; (2) longitudinal change due to tissue degeneration in brain disorders such as Huntington's Disease; (3) differences in anatomical images induced by changes in the tumor and surrounding structures during radiation treatment; and (4) dramatic effects of traumatic brain injury, intensity and shape of brain structures, and the effects of lesions on white matter connectivity.

==Geometric correspondence==
We are also developing tools for geometric correspondence between images, coordinate systems, shapes, and anatomies that are robust to anatomical and pathological variability. Establishing anatomical correspondences between pairs of patients, groups of patients, patients and templates, and individual patients over time is important for automatic and user-assisted image analysis. As with statistical models, state-of-art approaches typically rely on assumptions about geometric mappings or transformations, such as smoothness or invertibility, which make the analysis and computation more manageable. However, in applications that entail pathologies and thus more deformable anatomies, collections of anatomical objects can have very different shapes, topologies, and intensity boundary profiles. The ability to establish geometric correspondences, with and without expert guidance, in challenging clinical circumstances is essential for the DBPs. For example, in imaging patients with traumatic brain injuries, we propose to develop new methods to identify anatomy in the presence of large displacements and missing parts of organs and tissues, as well dramatic discrepancies in intensity or signal. For head and neck cancer imaging, the patient’s pose can dramatically affect the relative positions of tissues and organs, and for atrial fibrillation, physicians have requested comparisons of heart images taken before and after the process of remodeling. To address these problems, we will develop more flexible, general, and robust techniques for geometric correspondence.

==User interactive tools for segmentation==
New tools are needed for user-guided segmentation of tissues, organs, and lesions to enable clinicians to process images in very general circumstances. We are currently developing methodologies for patient-specific image segmentation that can be used in settings where the heterogeneity and variability of anatomy, pathology, and/or injury impedes the construction of conventional high level statistical models, but where users can see the structures of interest by observing contrast, lines, shapes, textures, or other image attributes - a development important for all DBPs. Our work will address the challenges of formulating the problem to capture important image properties and incorporate user input quickly and effectively. Beyond the DBPs, experience tell us that the range of medical and biological applications is so diverse that a set of reliable, light-weight, easy-to-use tools is a critical need. Furthermore, even when more automated analyses are feasible, they require some level of bootstrapping, using examples from segmentations that are driven by user interaction and low-level image features. We will address these problems by developing new formulations that make better use of user input and image features, new optimization strategies that are fast and effective, and new computational algorithms that run very efficiently on state-of-art computer architectures.

==Longitudinal and time-series analysis==
We are developing algorithms for statistical and geometric analysis that operate on multiple images of the same patient, or on collections of patients acquired over time. These algorithms are essential for patient-specific data analysis to assess how disease or injury progresses or
responds to treatment. Current cross-sectional analysis of longitudinal data does not provide a model of growth or change that considers the inherent correlation of repeated images of individuals, nor does it tell us how an individual patient changes relative to a change over time of a comparable healthy or disease-specific population, which often defines the basis for treatment planning. Two aspects are of particular importance for this project. First, when the progression or time behavior of a condition is an important component of the differences
between groups, the statistical power of comparisons benefit from subject-specific, time-dependent analysis. Second, the availability of longitudinal data presents an opportunity to leverage images at multiple time points for evaluations of shape and function. This adds a dynamic aspect to the process that can be useful in recognizing important aspects of disease or recovery. Longitudinal image analysis is important for all four DBPs in this project. The traumatic brain injury DBP, for instance, will monitor the progress of patients during recovery, and tools for systematically analyzing these changes will be essential. Likewise, the Head and Neck Cancer DBP, the Atrial Fibrillation DBP, and the Huntington’s Disease DBP all will require comparisons of patients across multiple time points, and the ability to consolidate these longitudinal models across collections of patients in comparison to healthy controls.
 

<gallery Caption="Algorithm Core Members" widths="100px" heights="75px" perrow="4">
image:SCI logo.jpg|R. Whitaker, G. Gerig, [http://www.sci.utah.edu SCI Institute], U of Utah
image:Csaillogo150.jpg|P. Golland, Eric Grimson, [http://www.csail.mit.edu Csail], MIT
image:UncLogo.png|M. Styner, [http://www.cs.unc.edu UNC]
image:Georgia-tech-logo.gif|A. Tannenbaum, [http://www.bme.gatech.edu BME], Georgia Tech
</gallery>

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